hypothesis and theory in research

This is the Difference Between a Hypothesis and a Theory

What to Know A hypothesis is an assumption made before any research has been done. It is formed so that it can be tested to see if it might be true. A theory is a principle formed to explain the things already shown in data. Because of the rigors of experiment and control, it is much more likely that a theory will be true than a hypothesis.

As anyone who has worked in a laboratory or out in the field can tell you, science is about process: that of observing, making inferences about those observations, and then performing tests to see if the truth value of those inferences holds up. The scientific method is designed to be a rigorous procedure for acquiring knowledge about the world around us.

In scientific reasoning, a hypothesis is constructed before any applicable research has been done. A theory, on the other hand, is supported by evidence: it's a principle formed as an attempt to explain things that have already been substantiated by data.

Toward that end, science employs a particular vocabulary for describing how ideas are proposed, tested, and supported or disproven. And that's where we see the difference between a hypothesis and a theory .

A hypothesis is an assumption, something proposed for the sake of argument so that it can be tested to see if it might be true.

In the scientific method, the hypothesis is constructed before any applicable research has been done, apart from a basic background review. You ask a question, read up on what has been studied before, and then form a hypothesis.

What is a Hypothesis?

A hypothesis is usually tentative, an assumption or suggestion made strictly for the objective of being tested.

When a character which has been lost in a breed, reappears after a great number of generations, the most probable hypothesis is, not that the offspring suddenly takes after an ancestor some hundred generations distant, but that in each successive generation there has been a tendency to reproduce the character in question, which at last, under unknown favourable conditions, gains an ascendancy. Charles Darwin, On the Origin of Species , 1859 According to one widely reported hypothesis , cell-phone transmissions were disrupting the bees' navigational abilities. (Few experts took the cell-phone conjecture seriously; as one scientist said to me, "If that were the case, Dave Hackenberg's hives would have been dead a long time ago.") Elizabeth Kolbert, The New Yorker , 6 Aug. 2007

What is a Theory?

A theory , in contrast, is a principle that has been formed as an attempt to explain things that have already been substantiated by data. It is used in the names of a number of principles accepted in the scientific community, such as the Big Bang Theory . Because of the rigors of experimentation and control, its likelihood as truth is much higher than that of a hypothesis.

It is evident, on our theory , that coasts merely fringed by reefs cannot have subsided to any perceptible amount; and therefore they must, since the growth of their corals, either have remained stationary or have been upheaved. Now, it is remarkable how generally it can be shown, by the presence of upraised organic remains, that the fringed islands have been elevated: and so far, this is indirect evidence in favour of our theory . Charles Darwin, The Voyage of the Beagle , 1839 An example of a fundamental principle in physics, first proposed by Galileo in 1632 and extended by Einstein in 1905, is the following: All observers traveling at constant velocity relative to one another, should witness identical laws of nature. From this principle, Einstein derived his theory of special relativity. Alan Lightman, Harper's , December 2011

Non-Scientific Use

In non-scientific use, however, hypothesis and theory are often used interchangeably to mean simply an idea, speculation, or hunch (though theory is more common in this regard):

The theory of the teacher with all these immigrant kids was that if you spoke English loudly enough they would eventually understand. E. L. Doctorow, Loon Lake , 1979 Chicago is famous for asking questions for which there can be no boilerplate answers. Example: given the probability that the federal tax code, nondairy creamer, Dennis Rodman and the art of mime all came from outer space, name something else that has extraterrestrial origins and defend your hypothesis . John McCormick, Newsweek , 5 Apr. 1999 In his mind's eye, Miller saw his case suddenly taking form: Richard Bailey had Helen Brach killed because she was threatening to sue him over the horses she had purchased. It was, he realized, only a theory , but it was one he felt certain he could, in time, prove. Full of urgency, a man with a mission now that he had a hypothesis to guide him, he issued new orders to his troops: Find out everything you can about Richard Bailey and his crowd. Howard Blum, Vanity Fair , January 1995

And sometimes one term is used as a genus, or a means for defining the other:

Laplace's popular version of his astronomy, the Système du monde , was famous for introducing what came to be known as the nebular hypothesis , the theory that the solar system was formed by the condensation, through gradual cooling, of the gaseous atmosphere (the nebulae) surrounding the sun. Louis Menand, The Metaphysical Club , 2001 Researchers use this information to support the gateway drug theory — the hypothesis that using one intoxicating substance leads to future use of another. Jordy Byrd, The Pacific Northwest Inlander , 6 May 2015 Fox, the business and economics columnist for Time magazine, tells the story of the professors who enabled those abuses under the banner of the financial theory known as the efficient market hypothesis . Paul Krugman, The New York Times Book Review , 9 Aug. 2009

Incorrect Interpretations of "Theory"

Since this casual use does away with the distinctions upheld by the scientific community, hypothesis and theory are prone to being wrongly interpreted even when they are encountered in scientific contexts—or at least, contexts that allude to scientific study without making the critical distinction that scientists employ when weighing hypotheses and theories.

The most common occurrence is when theory is interpreted—and sometimes even gleefully seized upon—to mean something having less truth value than other scientific principles. (The word law applies to principles so firmly established that they are almost never questioned, such as the law of gravity.)

This mistake is one of projection: since we use theory in general use to mean something lightly speculated, then it's implied that scientists must be talking about the same level of uncertainty when they use theory to refer to their well-tested and reasoned principles.

The distinction has come to the forefront particularly on occasions when the content of science curricula in schools has been challenged—notably, when a school board in Georgia put stickers on textbooks stating that evolution was "a theory, not a fact, regarding the origin of living things." As Kenneth R. Miller, a cell biologist at Brown University, has said , a theory "doesn’t mean a hunch or a guess. A theory is a system of explanations that ties together a whole bunch of facts. It not only explains those facts, but predicts what you ought to find from other observations and experiments.”

While theories are never completely infallible, they form the basis of scientific reasoning because, as Miller said "to the best of our ability, we’ve tested them, and they’ve held up."

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The Craft of Writing a Strong Hypothesis

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

• Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
• Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

• A research hypothesis has to be simple yet clear to look justifiable enough.
• It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
• It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
• A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
• If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
• A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

Hypothesis vs. Theory

A hypothesis is either a suggested explanation for an observable phenomenon, or a reasoned prediction of a possible causal correlation among multiple phenomena. In science , a theory is a tested, well-substantiated, unifying explanation for a set of verified, proven factors. A theory is always backed by evidence; a hypothesis is only a suggested possible outcome, and is testable and falsifiable.

Comparison chart

Examples of Theory and Hypothesis

Theory: Einstein's theory of relativity is a theory because it has been tested and verified innumerable times, with results consistently verifying Einstein's conclusion. However, simply because Einstein's conclusion has become a theory does not mean testing of this theory has stopped; all science is ongoing. See also the Big Bang theory , germ theory , and climate change .

Hypothesis: One might think that a prisoner who learns a work skill while in prison will be less likely to commit a crime when released. This is a hypothesis, an "educated guess." The scientific method can be used to test this hypothesis, to either prove it is false or prove that it warrants further study. (Note: Simply because a hypothesis is not found to be false does not mean it is true all or even most of the time. If it is consistently true after considerable time and research, it may be on its way to becoming a theory.)

This video further explains the difference between a theory and a hypothesis:

Common Misconception

People often tend to say "theory" when what they're actually talking about is a hypothesis. For instance, "Migraines are caused by drinking coffee after 2 p.m. — well, it's just a theory, not a rule."

This is actually a logically reasoned proposal based on an observation — say 2 instances of drinking coffee after 2 p.m. caused a migraine — but even if this were true, the migraine could have actually been caused by some other factors.

Because this observation is merely a reasoned possibility, it is testable and can be falsified — which makes it a hypothesis, not a theory.

• What is a Scientific Hypothesis? - LiveScience
• Wikipedia:Scientific theory

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Comments: Hypothesis vs Theory

Anonymous comments (2).

October 11, 2013, 1:11pm "In science, a theory is a well-substantiated, unifying explanation for a set of verified, proven hypotheses." But there's no such thing as "proven hypotheses". Hypotheses can be tested/falsified, they can't be "proven". That's just not how science works. Logical deductions based on axioms can be proven, but not scientific hypotheses. On top of that I find it somewhat strange to claim that a theory doesn't have to be testable, if it's built up from hypotheses, which DO have to be testable... — 80.✗.✗.139

May 6, 2014, 11:45pm "Evolution is a theory, not a fact, regarding the origin of living things." this statement is poorly formed because it implies that a thing is a theory until it gets proven and then it is somehow promoted to fact. this is just a misunderstanding of what the words mean, and of how science progresses generally. to say that a theory is inherently dubious because "it isn't a fact" is pretty much a meaningless statement. no expression which qualified as a mere fact could do a very good job of explaining the complicated process by which species have arisen on Earth over the last billion years. in fact, if you claimed that you could come up with such a single fact, now THAT would be dubious! everything we observe in nature supports the theory of evolution, and nothing we observe contradicts it. when you can say this about a theory, it's a pretty fair bet that the theory is correct. — 71.✗.✗.151

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• Key Differences

Know the Differences & Comparisons

Difference Between Hypothesis and Theory

The term ‘hypothesis’ is often contrasted with the term theory which implies an idea, typically proven, which aims at explaining facts and events. Both hypothesis and theory are important components of developing an approach, but these are not same. There exist a fine line of difference between hypothesis and theory, discussed in this article, have a look.

Content: Hypothesis Vs Theory

Comparison chart.

Definition of Hypothesis

An unproven statement or a mere assumption to be proved or disproved, about a factor, on which the researcher is interested, is called a hypothesis. It is a tentative statement, which is concerned with the relationship between two or more phenomena, as specified by the theoretical framework. The hypothesis has to go through a test, to determine its validity.

In other words, the hypothesis is a predictive statement, which can be objectively verified and tested through scientific methods, and relates the independent factor to the dependent one. To a researcher, a hypothesis is more like a question which he intends to resolve. The salient features of hypothesis are:

• It must be clear and precise or else the reliability of the inferences drawn will be questioned.
• It can be put to the test.
• If the hypothesis is relational, it should state the relationship between independent and dependent variables.
• The hypothesis should be open and responsive to testing within the stipulated time.
• It should be limited in scope and must be clearly defined.

Definition of Theory

An idea or a broad range of ideas that are assumed to be true, which aims at explaining cause and effect relationship between multiple observed phenomena. It is based on hypothesis, which after a thorough analysis and continuous testing and confirmation through observation and experiments, becomes a theory. As it is backed by evidence, it is scientifically proven.

Just like hypothesis, theories can also be accepted or rejected. As more and more information is gathered on the subject, theories are modified accordingly, to increase the accuracy of prediction over time.

Key Differences Between Hypothesis and Theory

The points given below are vital, so far as the difference between hypothesis and theory is concerned:

• Hypothesis refers to a supposition, based on few pieces of evidence, as an inception of further research or investigation. A theory is a well-affirmed explanation of natural phenomena, which is frequently validated through experimentation and observation.
• While the hypothesis is based on a little amount of data, the theory is based on a wide set of data.
• The hypothesis is an unproven statement; that can be tested. On the other hand, the theory is a scientifically tested and proven explanation of fact or event.
• Hypothesis relies on suggestions, prediction, possibility or projects whereas a theory is supported by evidence and is verified.
• The hypothesis may or may not be proved true, so the result is uncertain. On the contrary, the theory is one, that is assumed to be true and so its result is certain.
• Hypothesis and theory are two levels of the scientific method, i.e. theory follows hypothesis and the basis for research is hypothesis whose outcome is a theory.

Both hypothesis and theory are testable and falsifiable. When a hypothesis is proved true, by passing all critical tests and analysis, it becomes a theory. So, the hypothesis is very different from theory, as the former is something unproven but the latter is a proven and tested statement.

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BELLENS MOTEBEJANE says

July 15, 2019 at 2:31 pm

AMAIZING !WHAT ARE THE DIFFERENCE BETWEEN THEORY AND LAW?

February 17, 2022 at 3:47 am

Thanks, I’m finally clear on this for the first time in my life of 65 years

Curtis Le Gendre says

September 14, 2022 at 8:02 am

Great Information

Kenneth says

November 19, 2022 at 2:10 am

I was looking for some takes on this topic, and I found your article quite informative. It has given me a fresh perspective on the topic tackled. Thanks!

Stefanie Banis says

February 9, 2024 at 6:35 pm

Very informative! Thank you! I understand the difference much better now!

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Methodology

• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection .

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, other interesting articles, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

• An independent variable is something the researcher changes or controls.
• A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

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Step 1. ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2. Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

4. Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

5. Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in  if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis . The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

• H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
• H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

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• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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What is a Research Hypothesis: How to Write it, Types, and Examples

Any research begins with a research question and a research hypothesis . A research question alone may not suffice to design the experiment(s) needed to answer it. A hypothesis is central to the scientific method. But what is a hypothesis ? A hypothesis is a testable statement that proposes a possible explanation to a phenomenon, and it may include a prediction. Next, you may ask what is a research hypothesis ? Simply put, a research hypothesis is a prediction or educated guess about the relationship between the variables that you want to investigate.  

It is important to be thorough when developing your research hypothesis. Shortcomings in the framing of a hypothesis can affect the study design and the results. A better understanding of the research hypothesis definition and characteristics of a good hypothesis will make it easier for you to develop your own hypothesis for your research. Let’s dive in to know more about the types of research hypothesis , how to write a research hypothesis , and some research hypothesis examples .  

Table of Contents

What is a hypothesis ?  

A hypothesis is based on the existing body of knowledge in a study area. Framed before the data are collected, a hypothesis states the tentative relationship between independent and dependent variables, along with a prediction of the outcome.  

What is a research hypothesis ?  

Young researchers starting out their journey are usually brimming with questions like “ What is a hypothesis ?” “ What is a research hypothesis ?” “How can I write a good research hypothesis ?”   

A research hypothesis is a statement that proposes a possible explanation for an observable phenomenon or pattern. It guides the direction of a study and predicts the outcome of the investigation. A research hypothesis is testable, i.e., it can be supported or disproven through experimentation or observation.     

Characteristics of a good hypothesis  

Here are the characteristics of a good hypothesis :  

• Clearly formulated and free of language errors and ambiguity  
• Concise and not unnecessarily verbose  
• Has clearly defined variables  
• Testable and stated in a way that allows for it to be disproven  
• Can be tested using a research design that is feasible, ethical, and practical   
• Specific and relevant to the research problem  
• Rooted in a thorough literature search  
• Can generate new knowledge or understanding.  

How to create an effective research hypothesis  

A study begins with the formulation of a research question. A researcher then performs background research. This background information forms the basis for building a good research hypothesis . The researcher then performs experiments, collects, and analyzes the data, interprets the findings, and ultimately, determines if the findings support or negate the original hypothesis.  

Let’s look at each step for creating an effective, testable, and good research hypothesis :  

• Identify a research problem or question: Start by identifying a specific research problem.   
• Review the literature: Conduct an in-depth review of the existing literature related to the research problem to grasp the current knowledge and gaps in the field.   
• Formulate a clear and testable hypothesis : Based on the research question, use existing knowledge to form a clear and testable hypothesis . The hypothesis should state a predicted relationship between two or more variables that can be measured and manipulated. Improve the original draft till it is clear and meaningful.  
• State the null hypothesis: The null hypothesis is a statement that there is no relationship between the variables you are studying.   
• Define the population and sample: Clearly define the population you are studying and the sample you will be using for your research.  
• Select appropriate methods for testing the hypothesis: Select appropriate research methods, such as experiments, surveys, or observational studies, which will allow you to test your research hypothesis .  

Remember that creating a research hypothesis is an iterative process, i.e., you might have to revise it based on the data you collect. You may need to test and reject several hypotheses before answering the research problem.  

How to write a research hypothesis  

When you start writing a research hypothesis , you use an “if–then” statement format, which states the predicted relationship between two or more variables. Clearly identify the independent variables (the variables being changed) and the dependent variables (the variables being measured), as well as the population you are studying. Review and revise your hypothesis as needed.  

An example of a research hypothesis in this format is as follows:  

“ If [athletes] follow [cold water showers daily], then their [endurance] increases.”  

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

You may have understood the characteristics of a good hypothesis . But note that a research hypothesis is not always confirmed; a researcher should be prepared to accept or reject the hypothesis based on the study findings.  

Research hypothesis checklist  

Following from above, here is a 10-point checklist for a good research hypothesis :  

• Testable: A research hypothesis should be able to be tested via experimentation or observation.  
• Specific: A research hypothesis should clearly state the relationship between the variables being studied.  
• Based on prior research: A research hypothesis should be based on existing knowledge and previous research in the field.  
• Falsifiable: A research hypothesis should be able to be disproven through testing.  
• Clear and concise: A research hypothesis should be stated in a clear and concise manner.  
• Logical: A research hypothesis should be logical and consistent with current understanding of the subject.  
• Relevant: A research hypothesis should be relevant to the research question and objectives.  
• Feasible: A research hypothesis should be feasible to test within the scope of the study.  
• Reflects the population: A research hypothesis should consider the population or sample being studied.  
• Uncomplicated: A good research hypothesis is written in a way that is easy for the target audience to understand.  

By following this research hypothesis checklist , you will be able to create a research hypothesis that is strong, well-constructed, and more likely to yield meaningful results.  

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

A null hypothesis states that there is no change in the dependent variable due to changes to the independent variable. This means that the results are due to chance and are not significant. A null hypothesis is denoted as H0 and is stated as the opposite of what the alternative hypothesis states.   

Example: “ The newly identified virus is not zoonotic .”  

2. Alternative hypothesis:

This states that there is a significant difference or relationship between the variables being studied. It is denoted as H1 or Ha and is usually accepted or rejected in favor of the null hypothesis.  

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

This specifies the direction of the relationship or difference between variables; therefore, it tends to use terms like increase, decrease, positive, negative, more, or less.   

Example: “ The inclusion of intervention X decreases infant mortality compared to the original treatment .”   

4. Non-directional hypothesis:

While it does not predict the exact direction or nature of the relationship between the two variables, a non-directional hypothesis states the existence of a relationship or difference between variables but not the direction, nature, or magnitude of the relationship. A non-directional hypothesis may be used when there is no underlying theory or when findings contradict previous research.  

Example, “ Cats and dogs differ in the amount of affection they express .”  

5. Simple hypothesis :

A simple hypothesis only predicts the relationship between one independent and another independent variable.  

Example: “ Applying sunscreen every day slows skin aging .”  

6 . Complex hypothesis :

A complex hypothesis states the relationship or difference between two or more independent and dependent variables.   

Example: “ Applying sunscreen every day slows skin aging, reduces sun burn, and reduces the chances of skin cancer .” (Here, the three dependent variables are slowing skin aging, reducing sun burn, and reducing the chances of skin cancer.)  

7. Associative hypothesis:  

An associative hypothesis states that a change in one variable results in the change of the other variable. The associative hypothesis defines interdependency between variables.  

Example: “ There is a positive association between physical activity levels and overall health .”  

8 . Causal hypothesis:

A causal hypothesis proposes a cause-and-effect interaction between variables.  

Example: “ Long-term alcohol use causes liver damage .”  

Note that some of the types of research hypothesis mentioned above might overlap. The types of hypothesis chosen will depend on the research question and the objective of the study.  

Research hypothesis examples  

Here are some good research hypothesis examples :  

“The use of a specific type of therapy will lead to a reduction in symptoms of depression in individuals with a history of major depressive disorder.”  

“Providing educational interventions on healthy eating habits will result in weight loss in overweight individuals.”  

“Plants that are exposed to certain types of music will grow taller than those that are not exposed to music.”  

“The use of the plant growth regulator X will lead to an increase in the number of flowers produced by plants.”  

Characteristics that make a research hypothesis weak are unclear variables, unoriginality, being too general or too vague, and being untestable. A weak hypothesis leads to weak research and improper methods.   

Some bad research hypothesis examples (and the reasons why they are “bad”) are as follows:  

“This study will show that treatment X is better than any other treatment . ” (This statement is not testable, too broad, and does not consider other treatments that may be effective.)  

“This study will prove that this type of therapy is effective for all mental disorders . ” (This statement is too broad and not testable as mental disorders are complex and different disorders may respond differently to different types of therapy.)  

“Plants can communicate with each other through telepathy . ” (This statement is not testable and lacks a scientific basis.)  

Importance of testable hypothesis  

If a research hypothesis is not testable, the results will not prove or disprove anything meaningful. The conclusions will be vague at best. A testable hypothesis helps a researcher focus on the study outcome and understand the implication of the question and the different variables involved. A testable hypothesis helps a researcher make precise predictions based on prior research.  

To be considered testable, there must be a way to prove that the hypothesis is true or false; further, the results of the hypothesis must be reproducible.  

Frequently Asked Questions (FAQs) on research hypothesis  

1. What is the difference between research question and research hypothesis ?  

A research question defines the problem and helps outline the study objective(s). It is an open-ended statement that is exploratory or probing in nature. Therefore, it does not make predictions or assumptions. It helps a researcher identify what information to collect. A research hypothesis , however, is a specific, testable prediction about the relationship between variables. Accordingly, it guides the study design and data analysis approach.

2. When to reject null hypothesis ?

A null hypothesis should be rejected when the evidence from a statistical test shows that it is unlikely to be true. This happens when the test statistic (e.g., p -value) is less than the defined significance level (e.g., 0.05). Rejecting the null hypothesis does not necessarily mean that the alternative hypothesis is true; it simply means that the evidence found is not compatible with the null hypothesis.  

3. How can I be sure my hypothesis is testable?  

A testable hypothesis should be specific and measurable, and it should state a clear relationship between variables that can be tested with data. To ensure that your hypothesis is testable, consider the following:  

• Clearly define the key variables in your hypothesis. You should be able to measure and manipulate these variables in a way that allows you to test the hypothesis.  
• The hypothesis should predict a specific outcome or relationship between variables that can be measured or quantified.   
• You should be able to collect the necessary data within the constraints of your study.  
• It should be possible for other researchers to replicate your study, using the same methods and variables.   
• Your hypothesis should be testable by using appropriate statistical analysis techniques, so you can draw conclusions, and make inferences about the population from the sample data.  
• The hypothesis should be able to be disproven or rejected through the collection of data.  

4. How do I revise my research hypothesis if my data does not support it?  

If your data does not support your research hypothesis , you will need to revise it or develop a new one. You should examine your data carefully and identify any patterns or anomalies, re-examine your research question, and/or revisit your theory to look for any alternative explanations for your results. Based on your review of the data, literature, and theories, modify your research hypothesis to better align it with the results you obtained. Use your revised hypothesis to guide your research design and data collection. It is important to remain objective throughout the process.  

5. I am performing exploratory research. Do I need to formulate a research hypothesis?  

As opposed to “confirmatory” research, where a researcher has some idea about the relationship between the variables under investigation, exploratory research (or hypothesis-generating research) looks into a completely new topic about which limited information is available. Therefore, the researcher will not have any prior hypotheses. In such cases, a researcher will need to develop a post-hoc hypothesis. A post-hoc research hypothesis is generated after these results are known.  

6. How is a research hypothesis different from a research question?

A research question is an inquiry about a specific topic or phenomenon, typically expressed as a question. It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis.

7. Can a research hypothesis change during the research process?

Yes, research hypotheses can change during the research process. As researchers collect and analyze data, new insights and information may emerge that require modification or refinement of the initial hypotheses. This can be due to unexpected findings, limitations in the original hypotheses, or the need to explore additional dimensions of the research topic. Flexibility is crucial in research, allowing for adaptation and adjustment of hypotheses to align with the evolving understanding of the subject matter.

8. How many hypotheses should be included in a research study?

The number of research hypotheses in a research study varies depending on the nature and scope of the research. It is not necessary to have multiple hypotheses in every study. Some studies may have only one primary hypothesis, while others may have several related hypotheses. The number of hypotheses should be determined based on the research objectives, research questions, and the complexity of the research topic. It is important to ensure that the hypotheses are focused, testable, and directly related to the research aims.

9. Can research hypotheses be used in qualitative research?

Yes, research hypotheses can be used in qualitative research, although they are more commonly associated with quantitative research. In qualitative research, hypotheses may be formulated as tentative or exploratory statements that guide the investigation. Instead of testing hypotheses through statistical analysis, qualitative researchers may use the hypotheses to guide data collection and analysis, seeking to uncover patterns, themes, or relationships within the qualitative data. The emphasis in qualitative research is often on generating insights and understanding rather than confirming or rejecting specific research hypotheses through statistical testing.

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2.4 Developing a Hypothesis

Learning objectives.

• Distinguish between a theory and a hypothesis.
• Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
• Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Before describing how to develop a hypothesis it is imporant to distinguish betwee a theory and a hypothesis. A  theory  is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition. He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

Outside of science, referring to an idea as a theory often implies that it is untested—perhaps no more than a wild guess. In science, however, the term theory has no such implication. A theory is simply an explanation or interpretation of a set of phenomena. It can be untested, but it can also be extensively tested, well supported, and accepted as an accurate description of the world by the scientific community. The theory of evolution by natural selection, for example, is a theory because it is an explanation of the diversity of life on earth—not because it is untested or unsupported by scientific research. On the contrary, the evidence for this theory is overwhelmingly positive and nearly all scientists accept its basic assumptions as accurate. Similarly, the “germ theory” of disease is a theory because it is an explanation of the origin of various diseases, not because there is any doubt that many diseases are caused by microorganisms that infect the body.

A  hypothesis , on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate. It is an explanation that relies on just a few key concepts. Hypotheses are often specific predictions about what will happen in a particular study. They are developed by considering existing evidence and using reasoning to infer what will happen in the specific context of interest. Hypotheses are often but not always derived from theories. So a hypothesis is often a prediction based on a theory but some hypotheses are a-theoretical and only after a set of observations have been made, is a theory developed. This is because theories are broad in nature and they explain larger bodies of data. So if our research question is really original then we may need to collect some data and make some observation before we can develop a broader theory.

Theories and hypotheses always have this  if-then  relationship. “ If   drive theory is correct,  then  cockroaches should run through a straight runway faster, and a branching runway more slowly, when other cockroaches are present.” Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?” Thus deriving hypotheses from theories is an excellent way of generating interesting research questions.

But how do researchers derive hypotheses from theories? One way is to generate a research question using the techniques discussed in this chapter  and then ask whether any theory implies an answer to that question. For example, you might wonder whether expressive writing about positive experiences improves health as much as expressive writing about traumatic experiences. Although this  question  is an interesting one  on its own, you might then ask whether the habituation theory—the idea that expressive writing causes people to habituate to negative thoughts and feelings—implies an answer. In this case, it seems clear that if the habituation theory is correct, then expressive writing about positive experiences should not be effective because it would not cause people to habituate to negative thoughts and feelings. A second way to derive hypotheses from theories is to focus on some component of the theory that has not yet been directly observed. For example, a researcher could focus on the process of habituation—perhaps hypothesizing that people should show fewer signs of emotional distress with each new writing session.

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [1] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the  number  of examples they bring to mind and the other was that people base their judgments on how  easily  they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method  (although this term is much more likely to be used by philosophers of science than by scientists themselves). A researcher begins with a set of phenomena and either constructs a theory to explain or interpret them or chooses an existing theory to work with. He or she then makes a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researcher then conducts an empirical study to test the hypothesis. Finally, he or she reevaluates the theory in light of the new results and revises it if necessary. This process is usually conceptualized as a cycle because the researcher can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As  Figure 2.2  shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

Figure 2.2 Hypothetico-Deductive Method Combined With the General Model of Scientific Research in Psychology Together they form a model of theoretically motivated research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [2] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans (Zajonc & Sales, 1966) [3] in many other studies afterward).

Incorporating Theory into Your Research

When you write your research report or plan your presentation, be aware that there are two basic ways that researchers usually include theory. The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results. This format works well for applied research questions and for research questions that existing theories do not address. The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.

To use theories in your research will not only give you guidance in coming up with experiment ideas and possible projects, but it lends legitimacy to your work. Psychologists have been interested in a variety of human behaviors and have developed many theories along the way. Using established theories will help you break new ground as a researcher, not limit you from developing your own ideas.

Characteristics of a Good Hypothesis

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be  logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use  deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use  inductive reasoning  which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be  positive.  That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that really it does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

Key Takeaways

• A theory is broad in nature and explains larger bodies of data. A hypothesis is more specific and makes a prediction about the outcome of a particular study.
• Working with theories is not “icing on the cake.” It is a basic ingredient of psychological research.
• Like other scientists, psychologists use the hypothetico-deductive method. They construct theories to explain or interpret phenomena (or work with existing theories), derive hypotheses from their theories, test the hypotheses, and then reevaluate the theories in light of the new results.
• Practice: Find a recent empirical research report in a professional journal. Read the introduction and highlight in different colors descriptions of theories and hypotheses.
• Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic.  Journal of Personality and Social Psychology, 61 , 195–202. ↵
• Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach.  Journal of Personality and Social Psychology, 13 , 83–92. ↵
• Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵

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Scientific Hypothesis, Model, Theory, and Law

Understanding the Difference Between Basic Scientific Terms

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Words have precise meanings in science. For example, "theory," "law," and "hypothesis" don't all mean the same thing. Outside of science, you might say something is "just a theory," meaning it's a supposition that may or may not be true. In science, however, a theory is an explanation that generally is accepted to be true. Here's a closer look at these important, commonly misused terms.

A hypothesis is an educated guess, based on observation. It's a prediction of cause and effect. Usually, a hypothesis can be supported or refuted through experimentation or more observation. A hypothesis can be disproven but not proven to be true.

Example: If you see no difference in the cleaning ability of various laundry detergents, you might hypothesize that cleaning effectiveness is not affected by which detergent you use. This hypothesis can be disproven if you observe a stain is removed by one detergent and not another. On the other hand, you cannot prove the hypothesis. Even if you never see a difference in the cleanliness of your clothes after trying 1,000 detergents, there might be one more you haven't tried that could be different.

Scientists often construct models to help explain complex concepts. These can be physical models like a model volcano or atom  or conceptual models like predictive weather algorithms. A model doesn't contain all the details of the real deal, but it should include observations known to be valid.

Example: The  Bohr model shows electrons orbiting the atomic nucleus, much the same way as the way planets revolve around the sun. In reality, the movement of electrons is complicated but the model makes it clear that protons and neutrons form a nucleus and electrons tend to move around outside the nucleus.

A scientific theory summarizes a hypothesis or group of hypotheses that have been supported with repeated testing. A theory is valid as long as there is no evidence to dispute it. Therefore, theories can be disproven. Basically, if evidence accumulates to support a hypothesis, then the hypothesis can become accepted as a good explanation of a phenomenon. One definition of a theory is to say that it's an accepted hypothesis.

Example: It is known that on June 30, 1908, in Tunguska, Siberia, there was an explosion equivalent to the detonation of about 15 million tons of TNT. Many hypotheses have been proposed for what caused the explosion. It was theorized that the explosion was caused by a natural extraterrestrial phenomenon , and was not caused by man. Is this theory a fact? No. The event is a recorded fact. Is this theory, generally accepted to be true, based on evidence to-date? Yes. Can this theory be shown to be false and be discarded? Yes.

A scientific law generalizes a body of observations. At the time it's made, no exceptions have been found to a law. Scientific laws explain things but they do not describe them. One way to tell a law and a theory apart is to ask if the description gives you the means to explain "why." The word "law" is used less and less in science, as many laws are only true under limited circumstances.

Example: Consider Newton's Law of Gravity . Newton could use this law to predict the behavior of a dropped object but he couldn't explain why it happened.

As you can see, there is no "proof" or absolute "truth" in science. The closest we get are facts, which are indisputable observations. Note, however, if you define proof as arriving at a logical conclusion, based on the evidence, then there is "proof" in science. Some work under the definition that to prove something implies it can never be wrong, which is different. If you're asked to define the terms hypothesis, theory, and law, keep in mind the definitions of proof and of these words can vary slightly depending on the scientific discipline. What's important is to realize they don't all mean the same thing and cannot be used interchangeably.

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What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By:  Derek Jansen (MBA)  | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis 101

• What is a hypothesis ?
• What is a research hypothesis (scientific hypothesis)?
• Requirements for a research hypothesis
• Definition of a research hypothesis
• The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

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Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.  

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell. 

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

17 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

Hi” best wishes to you and your very nice blog” 

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when  conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a  correlational study  can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

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“Theory” vs. “Hypothesis”: What Is The Difference?

Chances are you’ve heard of the TV show The Big Bang Theory . Lots of people love this lighthearted sitcom for its quirky characters and their relationships, but others haven’t even given the series a chance for one reason: they don’t like science and assume the show is boring.

However, it only takes a few seconds with Sheldon and Penny to disprove this assumption and realize that this theory ab0ut The Big Bang Theory is wrong—it isn’t a scientific snoozefest.

But wait: is it a theory or a  hypothesis about the show that leads people astray? And would the actual big bang theory— the one that refers to the beginning of the universe—mean the same thing as a big bang hypothesis ?

Let’s take a closer look at theory and hypothesis to nail down what they mean.

What does theory mean?

As a noun, a theory is a group of tested general propositions “commonly regarded as correct, that can be used as principles of explanation and prediction for a class of phenomena .” This is what is known as a scientific   theory , which by definition is “an understanding that is based on already tested data or results .” Einstein’s theory of relativity and the  theory of evolution are both examples of such tested propositions .

Theory is also defined as a proposed explanation you might make about your own life and observations, and it’s one “whose status is still conjectural and subject to experimentation .” For example:  I’ve got my own theories about why he’s missing his deadlines all the time.  This example refers to an idea that has not yet been proven.

There are other uses of the word theory as well.

• In this example,  theory is “a body of principles or theorems belonging to one subject.” It can be a branch of science or art that deals with its principles or methods .
• For example: when she started to follow a new parenting theory based on a trendy book, it caused a conflict with her mother, who kept offering differing opinions .

First recorded in 1590–1600, theory originates from the Late Latin theōria , which stems from the Greek theōría. Synonyms for theory include approach , assumption , doctrine , ideology , method , philosophy , speculation , thesis , and understanding .

What does hypothesis mean?

Hypothesis is a noun that means “a proposition , or set of propositions, set forth as an explanation” that describe “some specified group of phenomena.” Sounds familiar to theory , no?

But, unlike a theory , a scientific  hypothesis is made before testing is done and isn’t based on results. Instead, it is the basis for further investigation . For example: her working hypothesis is that this new drug also has an unintended effect on the heart, and she is curious what the clinical trials  will show .

Hypothesis also refers to “a proposition assumed as a premise in an argument,” or “mere assumption or guess.” For example:

• She decided to drink more water for a week to test out her hypothesis that dehydration was causing her terrible headaches.
• After a night of her spouse’s maddening snoring, she came up with the hypothesis that sleeping on his back was exacerbating the problem.

Hypothesis was first recorded around 1590–1600 and originates from the Greek word hypóthesis (“basis, supposition”). Synonyms for hypothesis include: assumption , conclusion , conjecture , guess , inference , premise , theorem , and thesis .

How to use each

Although theory in terms of science is used to express something based on extensive research and experimentation, typically in everyday life, theory is used more casually to express an educated guess.

So in casual language,  theory and hypothesis are more likely to be used interchangeably to express an idea or speculation .

In most everyday uses, theory and hypothesis convey the same meaning. For example:

• Her opinion is just a theory , of course. She’s just guessing.
• Her opinion is just a hypothesis , of course. She’s just guessing.

It’s important to remember that a scientific   theory is different. It is based on tested results that support or substantiate it, whereas a hypothesis is formed before the research.

For example:

• His  hypothesis  for the class science project is that this brand of plant food is better than the rest for helping grass grow.
• After testing his hypothesis , he developed a new theory based on the experiment results: plant food B is actually more effective than plant food A in helping grass grow.

In these examples, theory “doesn’t mean a hunch or a guess,” according to Kenneth R. Miller, a cell biologist at Brown University. “A theory is a system of explanations that ties together a whole bunch of facts. It not only explains those facts, but predicts what you ought to find from other observations and experiments.”

So if you have a concept that is based on substantiated research, it’s a theory .

But if you’re working off of an assumption that you still need to test, it’s a hypothesis .

So remember, first comes a hypothesis , then comes theory . Now who’s ready for a  Big Bang Theory marathon?

Now that you’ve theorized and hypothesized through this whole article … keep testing your judgment (Or is it judgement?). Find out the correct spelling here!

Or find out the difference between these two common issues below!

WATCH: "Lethologica" vs. "Lethonomia": What's The Difference?

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The Research Hypothesis: Role and Construction

• First Online: 01 January 2012

Cite this chapter

• Phyllis G. Supino EdD 3  

6087 Accesses

A hypothesis is a logical construct, interposed between a problem and its solution, which represents a proposed answer to a research question. It gives direction to the investigator’s thinking about the problem and, therefore, facilitates a solution. There are three primary modes of inference by which hypotheses are developed: deduction (reasoning from a general propositions to specific instances), induction (reasoning from specific instances to a general proposition), and abduction (formulation/acceptance on probation of a hypothesis to explain a surprising observation).

A research hypothesis should reflect an inference about variables; be stated as a grammatically complete, declarative sentence; be expressed simply and unambiguously; provide an adequate answer to the research problem; and be testable. Hypotheses can be classified as conceptual versus operational, single versus bi- or multivariable, causal or not causal, mechanistic versus nonmechanistic, and null or alternative. Hypotheses most commonly entail statements about “variables” which, in turn, can be classified according to their level of measurement (scaling characteristics) or according to their role in the hypothesis (independent, dependent, moderator, control, or intervening).

A hypothesis is rendered operational when its broadly (conceptually) stated variables are replaced by operational definitions of those variables. Hypotheses stated in this manner are called operational hypotheses, specific hypotheses, or predictions and facilitate testing.

Wrong hypotheses, rightly worked from, have produced more results than unguided observation

—Augustus De Morgan, 1872[ 1 ]—

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Abductive Research Methods in Psychological Science

De Morgan A, De Morgan S. A budget of paradoxes. London: Longmans Green; 1872.

Google Scholar  

Leedy Paul D. Practical research. Planning and design. 2nd ed. New York: Macmillan; 1960.

Bernard C. Introduction to the study of experimental medicine. New York: Dover; 1957.

Erren TC. The quest for questions—on the logical force of science. Med Hypotheses. 2004;62:635–40.

Article   PubMed   Google Scholar  

Peirce CS. Collected papers of Charles Sanders Peirce, vol. 7. In: Hartshorne C, Weiss P, editors. Boston: The Belknap Press of Harvard University Press; 1966.

Aristotle. The complete works of Aristotle: the revised Oxford Translation. In: Barnes J, editor. vol. 2. Princeton/New Jersey: Princeton University Press; 1984.

Polit D, Beck CT. Conceptualizing a study to generate evidence for nursing. In: Polit D, Beck CT, editors. Nursing research: generating and assessing evidence for nursing practice. 8th ed. Philadelphia: Wolters Kluwer/Lippincott Williams and Wilkins; 2008. Chapter 4.

Jenicek M, Hitchcock DL. Evidence-based practice. Logic and critical thinking in medicine. Chicago: AMA Press; 2005.

Bacon F. The novum organon or a true guide to the interpretation of nature. A new translation by the Rev G.W. Kitchin. Oxford: The University Press; 1855.

Popper KR. Objective knowledge: an evolutionary approach (revised edition). New York: Oxford University Press; 1979.

Morgan AJ, Parker S. Translational mini-review series on vaccines: the Edward Jenner Museum and the history of vaccination. Clin Exp Immunol. 2007;147:389–94.

Article   PubMed   CAS   Google Scholar  

Pead PJ. Benjamin Jesty: new light in the dawn of vaccination. Lancet. 2003;362:2104–9.

Lee JA. The scientific endeavor: a primer on scientific principles and practice. San Francisco: Addison-Wesley Longman; 2000.

Allchin D. Lawson’s shoehorn, or should the philosophy of science be rated, ‘X’? Science and Education. 2003;12:315–29.

Article   Google Scholar  

Lawson AE. What is the role of induction and deduction in reasoning and scientific inquiry? J Res Sci Teach. 2005;42:716–40.

Peirce CS. Collected papers of Charles Sanders Peirce, vol. 2. In: Hartshorne C, Weiss P, editors. Boston: The Belknap Press of Harvard University Press; 1965.

Bonfantini MA, Proni G. To guess or not to guess? In: Eco U, Sebeok T, editors. The sign of three: Dupin, Holmes, Peirce. Bloomington: Indiana University Press; 1983. Chapter 5.

Peirce CS. Collected papers of Charles Sanders Peirce, vol. 5. In: Hartshorne C, Weiss P, editors. Boston: The Belknap Press of Harvard University Press; 1965.

Flach PA, Kakas AC. Abductive and inductive reasoning: background issues. In: Flach PA, Kakas AC, ­editors. Abduction and induction. Essays on their relation and integration. The Netherlands: Klewer; 2000. Chapter 1.

Murray JF. Voltaire, Walpole and Pasteur: variations on the theme of discovery. Am J Respir Crit Care Med. 2005;172:423–6.

Danemark B, Ekstrom M, Jakobsen L, Karlsson JC. Methodological implications, generalization, scientific inference, models (Part II) In: explaining society. Critical realism in the social sciences. New York: Routledge; 2002.

Pasteur L. Inaugural lecture as professor and dean of the faculty of sciences. In: Peterson H, editor. A treasury of the world’s greatest speeches. Douai, France: University of Lille 7 Dec 1954.

Swineburne R. Simplicity as evidence for truth. Milwaukee: Marquette University Press; 1997.

Sakar S, editor. Logical empiricism at its peak: Schlick, Carnap and Neurath. New York: Garland; 1996.

Popper K. The logic of scientific discovery. New York: Basic Books; 1959. 1934, trans. 1959.

Caws P. The philosophy of science. Princeton: D. Van Nostrand Company; 1965.

Popper K. Conjectures and refutations. The growth of scientific knowledge. 4th ed. London: Routledge and Keegan Paul; 1972.

Feyerabend PK. Against method, outline of an anarchistic theory of knowledge. London, UK: Verso; 1978.

Smith PG. Popper: conjectures and refutations (Chapter IV). In: Theory and reality: an introduction to the philosophy of science. Chicago: University of Chicago Press; 2003.

Blystone RV, Blodgett K. WWW: the scientific method. CBE Life Sci Educ. 2006;5:7–11.

Kleinbaum DG, Kupper LL, Morgenstern H. Epidemiological research. Principles and quantitative methods. New York: Van Nostrand Reinhold; 1982.

Fortune AE, Reid WJ. Research in social work. 3rd ed. New York: Columbia University Press; 1999.

Kerlinger FN. Foundations of behavioral research. 1st ed. New York: Hold, Reinhart and Winston; 1970.

Hoskins CN, Mariano C. Research in nursing and health. Understanding and using quantitative and qualitative methods. New York: Springer; 2004.

Tuckman BW. Conducting educational research. New York: Harcourt, Brace, Jovanovich; 1972.

Wang C, Chiari PC, Weihrauch D, Krolikowski JG, Warltier DC, Kersten JR, Pratt Jr PF, Pagel PS. Gender-specificity of delayed preconditioning by isoflurane in rabbits: potential role of endothelial nitric oxide synthase. Anesth Analg. 2006;103:274–80.

Beyer ME, Slesak G, Nerz S, Kazmaier S, Hoffmeister HM. Effects of endothelin-1 and IRL 1620 on myocardial contractility and myocardial energy metabolism. J Cardiovasc Pharmacol. 1995;26(Suppl 3):S150–2.

PubMed   CAS   Google Scholar  

Stone J, Sharpe M. Amnesia for childhood in patients with unexplained neurological symptoms. J Neurol Neurosurg Psychiatry. 2002;72:416–7.

Naughton BJ, Moran M, Ghaly Y, Michalakes C. Computer tomography scanning and delirium in elder patients. Acad Emerg Med. 1997;4:1107–10.

Easterbrook PJ, Berlin JA, Gopalan R, Matthews DR. Publication bias in clinical research. Lancet. 1991;337:867–72.

Stern JM, Simes RJ. Publication bias: evidence of delayed publication in a cohort study of clinical research projects. BMJ. 1997;315:640–5.

Stevens SS. On the theory of scales and measurement. Science. 1946;103:677–80.

Knapp TR. Treating ordinal scales as interval scales: an attempt to resolve the controversy. Nurs Res. 1990;39:121–3.

The Cochrane Collaboration. Open Learning Material. www.cochrane-net.org/openlearning/html/mod14-3.htm . Accessed 12 Oct 2009.

MacCorquodale K, Meehl PE. On a distinction between hypothetical constructs and intervening ­variables. Psychol Rev. 1948;55:95–107.

Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: ­conceptual, strategic and statistical considerations. J Pers Soc Psychol. 1986;51:1173–82.

Williamson GM, Schultz R. Activity restriction mediates the association between pain and depressed affect: a study of younger and older adult cancer patients. Psychol Aging. 1995;10:369–78.

Song M, Lee EO. Development of a functional capacity model for the elderly. Res Nurs Health. 1998;21:189–98.

MacKinnon DP. Introduction to statistical mediation analysis. New York: Routledge; 2008.

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Hypothesis vs. Theory: A Simple Guide to Tell Them Apart

By: Author ESLBUZZ

Posted on Last updated: July 27, 2023

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Hypothesis and theory are no stranger to those who conduct studies and work in science. These two terms are often used interchangeably by non-researchers, but they have distinct meanings in the scientific community. Understanding the difference between a hypothesis and a theory is essential for anyone interested in scientific research or critical thinking.

In this article, we will explore the differences between hypothesis and theory and provide examples to help you understand how they are used in scientific research. We will also discuss the importance of these terms in the scientific method and how they contribute to our understanding of the natural world. Whether you are a student, a researcher, or simply someone interested in science, this article will provide valuable insights into the world of scientific research.

To help illustrate the differences between hypothesis and theory, we will provide a comparison table that summarizes the key differences between these two terms and examples of how scientists use hypotheses and theories to explain natural phenomena and make predictions about future events. By the end of this article, you will have a clear understanding of the differences between hypothesis and theory and how they are used in scientific research.

Hypothesis vs. Theory

Hypothesis vs. Theory: Definitions

Understanding hypothesis.

A hypothesis is an educated guess or assumption that is made before conducting research. It is a tentative explanation for a phenomenon or observation that is based on limited evidence or prior knowledge. In other words, a hypothesis is a statement that proposes a relationship between two or more variables, which can be tested through further investigation.

Characteristics of Hypothesis

Hypotheses have certain characteristics that set them apart from other types of statements. These characteristics include:

• Testable: A hypothesis must be testable through empirical research. This means that it must be possible to collect data that can either support or refute the hypothesis.
• Specific: A hypothesis must be specific in its predictions. It should clearly state what is expected to happen and under what conditions.
• Falsifiable: A hypothesis must be falsifiable, which means that it must be possible to disprove the hypothesis if it is not supported by the evidence.
• Parsimonious: A hypothesis should be simple and straightforward. It should not include unnecessary assumptions or variables.

Examples of Hypothesis

Here are some examples of hypotheses:

• If a plant is exposed to sunlight, then it will grow faster than a plant that is not exposed to sunlight.
• If a person consumes more calories than they burn, then they will gain weight.
• If students are given more time to study for an exam, then they will perform better on the exam.

In summary, a hypothesis is an educated guess or assumption that is made before conducting research. It is testable, specific, falsifiable, and parsimonious. Examples of hypotheses include statements that propose a relationship between two or more variables, which can be tested through further investigation.

Understanding Theory

Definition of Theory

In scientific terms, a theory is a well-substantiated explanation of some aspect of the natural world that is based on empirical evidence. It is a collection of ideas that have been tested and confirmed through observation and experimentation. A theory is a framework that explains how and why things work in a certain way. It is a set of principles that can be used to make predictions about future events.

Characteristics of Theory

A theory has several characteristics that distinguish it from other scientific concepts such as hypotheses or laws. Some of the key characteristics of a theory are:

• A theory is based on empirical evidence and is supported by multiple lines of evidence.
• A theory is constantly evolving and can be modified or refined as new evidence emerges.
• A theory is generally accepted as true by the scientific community and is widely used to make predictions and guide research.
• A theory is not a guess or a hunch, but a well-substantiated explanation that has been rigorously tested.

Examples of Theory

There are many examples of well-established theories in science. Here are a few examples:

In summary, a theory is a well-substantiated explanation of some aspect of the natural world that is based on empirical evidence. It is a framework that explains how and why things work in a certain way and is constantly evolving as new evidence emerges. Theories are widely accepted as true by the scientific community and are used to make predictions and guide research.

Hypothesis vs. Theory: The Distinctions

As a writer, it is important to understand the differences between a hypothesis and a theory. These two scientific terms are often used interchangeably, but they have drastically different meanings in the world of science. In this section, we will explore the process of formulation, level of proof, and usage in the scientific community.

Process of Formulation

A hypothesis is an educated guess or assumption made before any research has been done. It is formed so that it can be tested to see if it might be true. Hypotheses are often based on observations or previous research and can be either proven or disproven through experimentation.

On the other hand, a theory is a well-established principle that is formed to explain the things already shown in data. Theories are based on a large body of evidence and have been extensively tested and proven through experimentation. The formulation of a theory requires a lot of research, experimentation, and analysis.

Level of Proof

The level of proof required for a hypothesis and a theory is vastly different. A hypothesis requires a certain level of proof to be considered valid, but it can still be disproven through experimentation. In contrast, a theory has been extensively tested and proven through experimentation, and therefore requires a much higher level of proof to be disproven.

Usage in Scientific Community

In the scientific community, hypotheses and theories play different roles. Hypotheses are used to generate predictions and testable explanations for phenomena, while theories are used to explain and predict a wide range of phenomena. Hypotheses are usually the starting point for research, while theories are the end result of extensive research and experimentation.

To summarize, a hypothesis is an educated guess or assumption made before any research has been done, while a theory is a well-established principle that is formed to explain the things already shown in data. Hypotheses require a certain level of proof to be considered valid, while theories require a much higher level of proof. In the scientific community, hypotheses are used to generate predictions and testable explanations for phenomena, while theories are used to explain and predict a wide range of phenomena.

Hypothesis vs. Theory: Common Misconceptions

When it comes to scientific research, there are several misconceptions about the differences between hypothesis and theory. In this section, we’ll explore some of the most common misconceptions and clarify the differences between these two scientific terms.

Misconception #1: Hypotheses are less important than theories

One common misconception is that hypotheses are less important than theories. This is not true. A hypothesis is the foundation of scientific research, as it is a proposed explanation for an observation or phenomenon. Without a hypothesis, there can be no scientific investigation.

Misconception #2: Hypotheses are guesses

Another common misconception is that hypotheses are guesses. While a hypothesis is an educated guess, it is not a random or arbitrary guess. A hypothesis is based on prior knowledge, observations, and data. It is a proposed explanation that can be tested through experimentation.

Misconception #3: Theories are proven facts

Many people believe that theories are proven facts. This is not true. A theory is a well-substantiated explanation for a set of observations or phenomena. It is based on a large body of evidence and has been repeatedly tested and confirmed through experimentation. However, theories are not absolute truths and are subject to revision or rejection based on new evidence.

Misconception #4: Hypotheses become theories

Some people believe that hypotheses become theories once they are proven. This is not true. A hypothesis can be supported or rejected by experimental evidence, but it does not become a theory. A theory is a broader explanation that encompasses many hypotheses and has been extensively tested and confirmed.

Misconception #5: Theories are more certain than hypotheses

Another common misconception is that theories are more certain than hypotheses. While theories are based on a large body of evidence and have been extensively tested, they are not absolute truths. Theories are subject to revision or rejection based on new evidence, just like hypotheses.

In summary, hypotheses and theories are both important components of scientific research. Hypotheses are proposed explanations that can be tested through experimentation, while theories are well-substantiated explanations that have been extensively tested and confirmed. While there are many misconceptions about the differences between hypotheses vs. theory, understanding these differences is crucial for conducting scientific research.

In conclusion, while the terms “hypothesis” and “theory” are often used interchangeably, they have distinct differences in the scientific method. A hypothesis is an assumption made before any research has been done, formed so that it can be tested to see if it might be true. On the other hand, a theory is a principle formed to explain the things already shown in data.

One way to differentiate between a hypothesis and a theory is to consider the level of evidence supporting each. A hypothesis is a proposed explanation for a phenomenon, but it is not yet supported by sufficient evidence. In contrast, a theory is a well-established explanation for a phenomenon that has been supported by a large body of evidence.

Another way to differentiate between a hypothesis and a theory is to consider their role in the scientific method. A hypothesis is an initial step in the scientific method, where a researcher formulates a testable prediction about a phenomenon. A theory, on the other hand, is the end result of the scientific method, where a researcher has tested and confirmed a hypothesis over time.

It is important to note that a hypothesis can eventually become a theory if it is repeatedly tested and supported by evidence. However, a theory can never become a hypothesis, as it is already a well-established explanation for a phenomenon.

In summary, understanding the differences between hypothesis and theory is crucial for conducting and interpreting scientific research. By using these terms correctly, researchers can communicate their ideas clearly and accurately, contributing to the advancement of scientific knowledge.

Frequently Asked Questions

How can you distinguish between hypothesis and theory?

A hypothesis is an educated guess or a proposed explanation for an observation or phenomenon. It is a tentative explanation that can be tested through experiments and observations. On the other hand, a theory is a well-established explanation that has been supported by a large body of evidence. The main difference between a hypothesis and a theory is that a hypothesis is a proposed explanation that needs to be tested, while a theory is a well-supported explanation that has been tested and confirmed by multiple lines of evidence.

What is the difference between a theory and a hypothesis in biology?

In biology, a hypothesis is a proposed explanation for a biological phenomenon that can be tested through experiments and observations. For example, a biologist might propose a hypothesis to explain why a particular species of bird has a particular beak shape. A theory in biology, on the other hand, is a well-established explanation that has been supported by a large body of evidence. For example, the theory of evolution is a well-established explanation for the diversity of life on Earth.

What is an example of a theory statement?

A theory statement is a statement that summarizes a well-established explanation for a phenomenon. For example, the theory of relativity is a statement that summarizes Einstein’s well-established explanation for the behavior of objects in space and time.

How are hypotheses and theories similar and different?

Both hypotheses and theories are proposed explanations for phenomena. However, while hypotheses are tentative and need to be tested, theories are well-established and have been supported by a large body of evidence. In addition, hypotheses are often specific to a particular observation or phenomenon, while theories are more general and can explain a wide range of phenomena.

What are some examples of the differences between a hypothesis and a theory?

An example of a hypothesis might be that a particular drug will cure a particular disease. An example of a theory might be the theory of plate tectonics, which explains the movement of the Earth’s crust. The main difference between these two examples is that the first is a tentative explanation that needs to be tested, while the second is a well-established explanation that has been supported by a large body of evidence.

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A theory statement is a statement that summarizes a well-established explanation for a phenomenon. For example, the theory of relativity is a statement that summarizes Einstein's well-established explanation for the behavior of objects in space and time.

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An example of a hypothesis might be that a particular drug will cure a particular disease. An example of a theory might be the theory of plate tectonics, which explains the movement of the Earth's crust. The main difference between these two examples is that the first is a tentative explanation that needs to be tested, while the second is a well-established explanation that has been supported by a large body of evidence.

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Research Questions vs Hypothesis: What’s The Difference?

by  Antony W

August 1, 2024

You’ll need to come up with a research question or a hypothesis to guide your next research project. But what is a hypothesis in the first place? What is the perfect definition for a research question? And, what’s the difference between the two?

In this guide to research questions vs hypothesis, we’ll look at the definition of each component and the difference between the two.

We’ll also look at when a research question and a hypothesis may be useful and provide you with some tips that you can use to come up with hypothesis and research questions that will suit your research topic . 

Let’s get to it.

What’s a Research Question?

We define a research question as the exact question you want to answer on a given topic or research project. Good research questions should be clear and easy to understand, allow for the collection of necessary data, and be specific and relevant to your field of study.

Research questions are part of heuristic research methods, where researchers use personal experiences and observations to understand a research subject. By using such approaches to explore the question, you should be able to provide an analytical justification of why and how you should respond to the question. 

While it’s common for researchers to focus on one question at a time, more complex topics may require two or more questions to cover in-depth.

When is a Research Question Useful? 

A research question may be useful when and if: 

• There isn’t enough previous research on the topic
• You want to report a wider range out of outcome when doing your research project
• You want to conduct a more open ended inquiries 

Perhaps the biggest drawback with research questions is that they tend to researchers in a position to “fish expectations” or excessively manipulate their findings.

Again, research questions sometimes tend to be less specific, and the reason is that there often no sufficient previous research on the questions.

What’s a Hypothesis? 

A hypothesis is a statement you can approve or disapprove. You develop a hypothesis from a research question by changing the question into a statement.

Primarily applied in deductive research, it involves the use of scientific, mathematical, and sociological findings to agree to or write off an assumption.

Researchers use the null approach for statements they can disapprove. They take a hypothesis and add a “not” to it to make it a working null hypothesis.

A null hypothesis is quite common in scientific methods. In this case, you have to formulate a hypothesis, and then conduct an investigation to disapprove the statement.

If you can disapprove the statement, you develop another hypothesis and then repeat the process until you can’t disapprove the statement.

In other words, if a hypothesis is true, then it must have been repeatedly tested and verified.

The consensus among researchers is that, like research questions, a hypothesis should not only be clear and easy to understand but also have a definite focus, answerable, and relevant to your field of study. 

When is a Hypothesis Useful?

A hypothesis may be useful when or if:

• There’s enough previous research on the topic
• You want to test a specific model or a particular theory
• You anticipate a likely outcome in advance 

The drawback to hypothesis as a scientific method is that it can hinder flexibility, or possibly blind a researcher not to see unanticipated results.

Research Question vs Hypothesis: Which One Should Come First 

Researchers use scientific methods to hone on different theories. So if the purpose of the research project were to analyze a concept, a scientific method would be necessary.

Such a case requires coming up with a research question first, followed by a scientific method.

Since a hypothesis is part of a research method, it will come after the research question.

Research Question vs Hypothesis: What’s the Difference? 

The following are the differences between a research question and a hypothesis.

We look at the differences in purpose and structure, writing, as well as conclusion. 

Research Questions vs Hypothesis: Some Useful Advice 

As much as there are differences between hypothesis and research questions, you have to state either one in the introduction and then repeat the same in the conclusion of your research paper.

Whichever element you opt to use, you should clearly demonstrate that you understand your topic, have achieved the goal of your research project, and not swayed a bit in your research process.

If it helps, start and conclude every chapter of your research project by providing additional information on how you’ve or will address the hypothesis or research question.

You should also include the aims and objectives of coming up with the research question or formulating the hypothesis. Doing so will go a long way to demonstrate that you have a strong focus on the research issue at hand. 

Research Questions vs Hypothesis: Conclusion 

If you need help with coming up with research questions, formulating a hypothesis, and completing your research paper writing , feel free to talk to us. 

About the author 

Antony W is a professional writer and coach at Help for Assessment. He spends countless hours every day researching and writing great content filled with expert advice on how to write engaging essays, research papers, and assignments.

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Difference Between Theory and Hypothesis

Many of them belittle evolution because “it is just a theory.” Gravity, on the other hand, must be real because it is a law. The words “theory,” “facts,” “laws” and “hypothesis” have a very specific meaning in the scientific world that doesn’t quite match the ones we use in everyday language. A hypothesis is a tentative explanation of an observation that can be tested. It acts as a starting point for further explanation. Theory, on the other hand, is an explanation of some aspect of the natural world that’s well-justified by facts, tested hypotheses, and laws. Let us look at more differences between hypothesis and theory given in a tabular column below.

Theory vs Hypothesis

From the above differences, we can infer that a hypothesis might change significantly as the testing occurs. A hypothesis can either be right or wrong. When a hypothesis is tested and proved true, it becomes a theory. At BYJU’S, learn more differences like the difference between asteroid and comet.

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HYPOTHESIS AND THEORY article

From diagnosis to dialogue – reconsidering the dsm as a conversation piece in mental health care: a hypothesis and theory.

• 1 Department of Psychiatry, Utrecht University Medical Center, Utrecht, Netherlands
• 2 Digital Innovation in Health, NHL Stenden University of Applied Sciences, Leeuwarden, Netherlands
• 3 Department of Research and Innovation, KieN VIP Mental Health Care Services, Leeuwarden, Netherlands
• 4 Department of Child and Family Welfare, University of Groningen, Groningen, Netherlands

The Diagnostic and Statistical Manual of Mental Disorders, abbreviated as the DSM, is one of mental health care’s most commonly used classification systems. While the DSM has been successful in establishing a shared language for researching and communicating about mental distress, it has its limitations as an empirical compass. In the transformation of mental health care towards a system that is centered around shared decision-making, person-centered care, and personal recovery, the DSM is problematic as it promotes the disengagement of people with mental distress and is primarily a tool developed for professionals to communicate about patients instead of with patients. However, the mental health care system is set up in such a way that we cannot do without the DSM for the time being. In this paper, we aimed to describe the position and role the DSM may have in a mental health care system that is evolving from a medical paradigm to a more self-contained profession in which there is increased accommodation of other perspectives. First, our analysis highlights the DSM’s potential as a boundary object in clinical practice, that could support a shared language between patients and professionals. Using the DSM as a conversation piece, a language accommodating diverse perspectives can be co-created. Second, we delve into why people with lived experience should be involved in co-designing spectra of distress. We propose an iterative design and test approach for designing DSM spectra of distress in co-creation with people with lived experience to prevent the development of ‘average solutions’ for ‘ordinary people’. We conclude that transforming mental health care by reconsidering the DSM as a boundary object and conversation piece between activity systems could be a step in the right direction, shifting the power balance towards shared ownership in a participation era that fosters dialogue instead of diagnosis.

1 Introduction

The Diagnostic Statistical Manual of Mental Disorders (DSM) has great authority in practice. The manual, released by the American Psychiatric Association (APA), provides a common language and a classification system for clinicians to communicate about people’s experiences of mental distress and for researchers to study social phenomena that include mental distress and its subsequent treatments. Before the DSM was developed, a plethora of mental health-related documents circulated in the United States ( 1 ). In response to the confusion that arose from this diversity of documents, the APA Committee on Nomenclature and Statistics standardized these into one manual, the DSM-I ( 2 ). In this first edition of the manual, released in 1952, mental distress was understood as a reaction to stress caused by psychological and interpersonal factors in the person’s life ( 3 ). Although the DSM-I had limited impact on practice ( 4 ), it did set the stage for increasingly standardized categorization of mental disorders ( 5 ).

The DSM-II was released in 1968. In this second iteration, mental disorders were understood as the patient’s attempts to control overwhelming anxiety with unconscious, intrapsychic conflicts ( 3 ). In this edition, the developers attempted to describe the symptoms of disorders and define their etiologies. They had chosen to base them predominantly on psychodynamic psychiatry but also included the biological focus of Kraepelin’s system of classification ( 5 , 6 ). During the development of the DSM-III, the task force added the goal to improve the reliability — the likelihood that different professionals arrive at the same diagnosis — of psychiatric diagnosis, which now became an important feature of the design process. The developers abandoned the psychodynamic view and shifted the focus to atheoretical descriptions, aiming to specify objective criteria for diagnosing mental disorders ( 3 ). Although it was explicitly stated in DSM-III that there was no underlying assumption that the categories were validated entities ( 7 ), the categorical approach still assumed each pattern of symptoms in a category reflected an underlying pathology. The definition of ‘mental illness’ was thereby altered from what one did or was (“you react anxious/you are anxious”) to something one had (“you have anxiety”). This resulted in descriptive, criteria-based classifications that reflected a perceived need for standardization of psychiatric diagnoses ( 5 , 6 ). The DSM-III was released in 1980 and had a big impact on practice ( 6 ) as it inaugurated an attempt to “re-medicalize” American psychiatry ( 5 ).

In hindsight, it is not surprising that after the release of the DSM-III, the funding for psychopharmacological research skyrocketed ( 8 ). At the same time, the debate on the relationship between etiology and description in psychiatric diagnosis continued ( 9 ). As sociologist Andrew Scull ( 10 ) showed, the election of President Reagan prompted a shift towards a focus on biology. His successor, President Bush, claimed that the 1990s were ‘the decade of the brain,’ which fueled a sharp increase in funding for research on genetics and neuroscience ( 10 ). Despite the public push for biological research, the DSM-IV aimed to arrive at a purely atheoretical description of psychiatric diagnostic criteria and was released in 1994 ( 11 ). The task force conducted multi-center field trials to relate diagnoses to clinical practice to improve reliability, which remained a goal of the design process ( 12 ). While the DSM-IV aimed to be atheoretical, researchers argued that the underlying ontologies were easily deducible from their content: psychological and social causality were eliminated and replaced implicitly with biological causality ( 13 ). In the DSM-5, validity — whether a coherent syndrome is being measured and whether it is what it is assumed to be — took center stage ( 10 ). The definition of mental disorder in the DSM-5 was thereby conceptualized as:

“… a syndrome characterized by clinically significant disturbance in an individual’s cognition, emotion regulation, or behavior that reflects a dysfunction in the psychological, biological, or developmental processes underlying mental functioning.” ( 14 ).

With the release of the DSM-5, the debate surrounding the conceptualization of mental distress started all over again, but this can be best seen as re-energizing longstanding debates around the utility and validity of APA nosology ( 15 ). Three important design goals from the DSM-III until current editions can be observed: providing an international language on mental distress, developing a reliable classification system, and creating a valid classification system.

1.1 The limitations of the DSM as an empirical compass

The extent to which these three design goals were attained is only partial. The development of an international language has been accomplished, as the DSM (as well as the International Classification of Diseases) is now widely employed across most Western countries. Although merely based on consensus, the DSM enables — to an extent — professionals and researchers to quantify the prevalence of certain behaviors and find one or more classifications that best suit these observed behaviors. To this date, the expectation that diagnostic criteria would be empirically validated through research has not yet been fulfilled ( 10 , 16 , 17 ). As stated by the authors of the fourth edition ( 11 ), the disorders listed in the DSM are “valuable heuristic constructs” that serve a purpose in research and practice. However, it was already emphasized in the DSM-IV guidebook that they do not precisely depict nature as it is, being characterized as not “well-defined entities” ( 18 ). Furthermore, while the fifth edition refers to “syndromes,” it is again described that “there is no assumption that each category of mental disorder is a completely discrete entity with absolute boundaries dividing it from other mental disorders or from no mental disorder” ( 14 ). Consequently, there are no laboratory tests or biological markers to set the boundary between ‘normal’ and ‘pathological,’ thus, it cannot confirm or reject the presumed pathologies underlying the DSM classifications, thereby rendering the validity goal of the design unattained. Therefore, the reliability of the current major DSM (i.e., DSM-5) still raises concerns ( 19 ).

By focusing conceptually on mental distress as an individual experience, the DSM task forces have neglected the role of social context, potentially restricting a comprehensive clinical understanding of mental distress ( 20 ). There is mounting evidence and increased attention, however, that the social environment, including its determinants and factors, is crucial for the onset, course, and outcome of mental distress ( 21 – 27 ). Moreover, exposure to factors such as early life adversity, poverty, unemployment, trauma, and minority group position is strongly associated with the onset of mental distress ( 28 , 29 ). It is also established that the range of ontological perspectives — what mental distress is and how it exists — is far broader than what is typically covered in prevailing scientific and educational discussions ( 30 ). These diverse perspectives are also evident in the epistemic pluralism among theoretical models on mental health problems ( 31 ).

1.2 The DSM is problematic in the transformation of mental health care

In the context of contemporary transformations in mental health care, the role of the DSM as an empirical instrument becomes even more problematic. In recent years, significant shifts have been witnessed in mental health care services, with a growing focus on promoting mental well-being, preventive measures, and person-centered and rights-based approaches ( 32 ). In contrast to the 1950s definition of health in which health was seen as the absence of disease, health today is defined as “the ability to adapt and to self-manage” ( 33 ), also known as ‘positive health.’ Furthermore, the recovery movement ( 34 ), person-centered care ( 35 ), and the integration of professionals’ lived experiences ( 36 ) all contributed to a more person-centered mental health care that promotes shared-decision making as a fundamental principle in practice in which no one perspective holds the wisdom. Shared decision-making is “an approach where clinicians and patients share the best available evidence when faced with the task of making decisions, and where patients are supported to consider options, to achieve informed preferences” ( 37 ). To realize and enable a more balanced relationship between professional and patient in shared decision-making, the interplay of healthcare professionals’ and patients’ skills, the support for a patient, and a good relationship between professional and patient are important to facilitate patients’ autonomy ( 38 ). Thus, mental health care professionals in the 21st century should collaborate, embrace ideography, and maximize effects mediated by therapeutic relationships and the healing effects of ritualized care interactions ( 39 ).

The DSM and its designed classifications, as well as their use in the community, can hinder a person-centered approach in which meaning is collaboratively derived for mental health issues, where a balanced relationship is needed, and where decisions are made together. We can demonstrate this with a brief example involving the ADHD classification and its criteria, highlighting how its design tends to marginalize individuals with mental distress, reducing their behavior to objectification from the clinician’s viewpoint. The ADHD classification delineates an ideal self that highly esteems disengagement from one’s feelings and needs, irrespective of contextual factors ( 40 ). This inclination is apparent in the criteria, including criterion 1a concerning inattention: “often avoids, dislikes, or is reluctant to engage in tasks that require sustained mental effort”. This indicates that disliking something is viewed as a symptom rather than a personal preference ( 40 ). Due to a lack of attention to the person’s meaning, a behavior that may be a preference of the individual can become a symptom of a disease. Another instance can be observed in criterion 2c: “often runs about or climbs in situations where it is inappropriate.” Although such behavior might be deemed inappropriate in certain contexts, many individuals derive enjoyment from running and climbing. In this way, ‘normal’ human behavior can be pathologized because there is no room for the meaning of the individual.

A parallel disengagement is evident in the DSM’s viewpoint on individuals with mental distress ( 40 ), as the diagnostic process appears to necessitate no interaction with an individual; instead, it fosters disengagement rather than engagement. For example, according to the DSM-5, when a child is “engaged in especially interesting activities,” the clinician is warned that the ‘symptoms’ may not manifest. Although it appears most fitting to assist the child by exploring their interests, clinicians are instead encouraged to seek situations the child finds uninteresting and assess whether the child can concentrate ( 40 ). If the child cannot concentrate, a ‘diagnosis’ might be made, and intervention can be initiated. This highlights that the design of the DSM promotes professionals to locate individual disorders in a person at face value without considering contextual factors, personal preferences, or other idiosyncrasies in a person’s present or history ( 41 ). It is also apparent that the term ‘symptom’ in the DSM implies an underlying entity as its cause, obscuring that it is a subjective criterion based on human assessment and interpretation ( 42 ). These factors make it difficult for the DSM in its current form to have a place in person-centered mental health care that promotes shared decision-making.

1.3 The problem and hypotheses

Diagnostic manuals like the DSM function similarly to standard operating procedures: they streamline decision-making and assist professionals in making approximate diagnoses when valid and specific measures are lacking or not readily accessible ( 43 ). However, the DSM is often (mis)used as a manual providing explanations for mental distress. This hinders a personalized approach that prioritizes the patient’s needs. Furthermore, this approach does not align with the principles of shared decision-making, as the best available evidence indicates that classifications are not explanations for mental distress. Also, disengagement is promoted in the design of the DSM, which is problematic in the person-centered transformation of mental health care in which a range of perspectives and human-centered interventions are needed. This paper aims to describe the position and role the DSM may have in a mental health care system that is evolving from a medical paradigm to a more self-contained profession in which there is increased accommodation of other perspectives. For this hypothesis and theory paper, we have formulated the following hypotheses:

(1) Reconsidering the DSM as a boundary object that can be used as a conversation piece allows for other perspectives on what is known about mental distress and aligns with the requirements of person-centered mental health care needed for shared decision-making;.

(2) Embracing design approaches in redesigning the DSM to a conversation piece that uses spectra of mental distress instead of classifications will stimulate the integration of diverse perspectives and voices in reshaping mental health care.

2 Co-creation of a real common language

The DSM originally aimed to develop a common language, and it has achieved that to some extent, but it now primarily serves as a common language among professionals. This does not align with the person-centered transformation in mental health care, where multiple perspectives come into play ( 32 , 44 ). In this section, we will address our first hypothesis: reconsidering the DSM as a boundary object that can be used as a conversation piece allows for other perspectives on what is known about mental distress and aligns with the requirements of person-centered mental health care needed for shared decision-making. First, we will examine several unintended consequences of classifications. After that, we propose considering the DSM as boundary objects to arrive at a real common language in which the perspective of people with lived experience is promoted. This perspective views the DSM as a conversation piece that can be used as a subject, the meaning of which can be attributed from various perspectives where the premise is that there is not an omniscient perspective.

2.1 Validation, stigma, and making up people

Classifications influence what we see or do not see, what is valorized, and what is silenced ( 45 ). DSM classifications and the process of getting them can provide validation and relief for some service users, while for others, it can be stigmatizing and distressing ( 46 , 47 ). The stigma people encounter can be worse than the mental problems themselves ( 48 ). The classification of people’s behaviors is not simply a passive reflection of pre-existing characteristics but is influenced by social and cultural factors. The evolution of neurasthenia serves as a fascinating illustration of the notable ontological changes in the design of the DSM, constantly reflecting and constructing reality. Initially, neurasthenia was considered a widespread mental disorder with presumed somatic roots. Still, it was subsequently discarded from use, only to resurface several decades later as a culture-bound manifestation of individual mental distress ( 49 ). Consequently, certain mental disorders, as depicted in the DSM, may not have existed in the same way as before the classifications were designed. This has been called ‘making up people’, which entails the argument that different kinds of human beings and human acts come into being hand in hand with our invention of the categories labeling them ( 50 ). Furthermore, it is important to consider that whether behavior is deemed dysfunctional or functional is always influenced by the prevailing norms and traditions within a specific society at a given time. Therefore, the individual meaning of the patient in its context is always more important than general descriptions and criteria of functional and dysfunctional behavior (i.e., ADHD climbing example).

Individuals might perceive themselves differently and develop emotions and behaviors partly due to the classifications imposed upon them. Over time, this can result in alterations to the classification itself, a phenomenon referred to as the classificatory looping effect ( 51 ). Moreover, when alterations are made to the world that align with the system’s depiction of reality, ‘blindness’ can occur ( 45 ). To illustrate, let’s consider an altered scenario of Bowker and Star ( 45 ) in which all mental distress is categorized solely based on physiological factors. In this context, medical frameworks for observation and treatment are designed to recognize physical manifestations of distress, such as symptoms, and the available treatments are limited to physical interventions, such as psychotropic medications. Consequently, in such a design, mental distress may solely be a consequence of a chemical imbalance in the brain, making it nearly inconceivable to consider alternative conceptualizations or solutions. Thus, task forces responsible for designing mental disorder classifications should be acutely aware that they actively contribute to the co-creation of reality with the classifications they construct upon reality ( 49 ).

2.2 Reification and disorderism

Another unintended consequence is the reification of classifications. Reification involves turning a broad and potentially diverse range of human experiences into a fixed and well-defined category. Take, for example, the case of the classification of ADHD and its reification mechanisms (i.e., language choice, logical fallacies, genetic reductionism, and textual silence) ( 42 ). Teachers sometimes promote the classification of ADHD as they believe it acknowledges a prior feeling that something is the matter with a pupil. The classification is then seen as a plausible explanation for the emergence of specific behaviors, academic underperformance, or deviations from the expected norm within a peer group ( 52 , 53 ). At first glance, this may seem harmless. However, it reinforces the notion that a complex and multifaceted set of contextual behaviors, experiences, and psychological phenomena are instead a discrete, objective entity residing in the individual. This is associated with presuppositions in the DSM that are not explicitly articulated, such as attributing a mental disorder to the individual rather than the system, resulting in healthcare that is organized around the individual instead of organized around the system ( 54 ).

In this way, DSM classifications can decontextualize mental distress, leading to ‘disorderism’. Disorderism is defined as the systemic decontextualization of mental distress by framing it in terms of individual disorders ( 55 ). The processes by which people are increasingly diagnosed and treated as having distinct treatable individual disorders, exemplified by the overdiagnosis of ADHD in children and adolescents ( 56 ), while at the same time, the services of psychiatry shape more areas of life, has been called the ‘psychiatrization of society’ ( 57 ). The psychiatrization of society encompasses a pervasive influence whereby the reification and disorderism extend beyond clinical settings and infiltrate various facets of daily life. It is a double-edged sword that fosters increased awareness of mental health issues and seeks to reduce stigma, but at the same time, raises concerns about the overemphasis on medical models, potentially neglecting the broader social, cultural, and environmental factors that contribute to individual well-being as well as population salutogenesis ( 58 ).

2.3 The DSM as a boundary object between activity systems in clinical practice

Instead of using the DSM as a scientific and professional tool in order to classify, the DSM can be reconsidered as a boundary object. When stakeholders with different objectives and needs have to work together constructively without making concessions, like patients and professionals in person-centered mental health care, objects can play a bridging role. Star and Griesemer ( 59 ) introduced the term boundary objects for this purpose.

“Boundary objects are objects that are plastic enough to adapt to the local needs and constraints of the different parties using them, yet robust enough to maintain a common identity in different locations. They are weakly structured in common use and become strongly structured in use in individual locations. They can be abstract or concrete. They have different meanings in different social worlds, but their structure is common enough to more than one world to make them recognizable, a means of translation.” ( 59 ).

Before exploring the benefits of a boundary object perspective for the DSM, it is important to note that it remains questionable whether the DSM in its current form can help establish a shared understanding or provide diagnostic, prognostic, or therapeutic value ( 60 – 63 ). To make the DSM more suitable for accommodating different perspectives and types of knowledge, the DSM task force can focus its redesign on leaving the discrete disease entities — which classifications imply — behind by creating spectra. This way of thinking has already found its way to the DSM-5, in which mental distress as a spectrum was introduced in the areas of autism, substance use, and nearly personality disorders, and following these reconceptualization, also a psychosis spectrum was proposed ( 43 ), but this proposition was eventually not adopted in the manual. As mental distress can be caused by an extensive range of factors and mechanisms that result from interactions in networks of behaviors and patterns that have complex dynamics that unfold over time ( 64 ), spectra of mental distress may be more suitable for conversations about an individual’s narrative and needs in clinical practice, as each experience of mental distress is unique and contextual.

If the DSM is reconsidered as a boundary object that is intended to provide a shared language for interpreting mental distress while addressing the unintended consequences of classifications, it is also essential to consider where this language now primarily manifests itself, how it relates to shared decision-making, and the significant role it plays for patients in the treatment process. In recent decades, the DSM has positioned itself primarily as a professional tool for clinical judgment (see Figure 1 ). In this way, professionals have more or less acquired a monopoly on the language of classifications and the associated behaviors and complaints described in the DSM. It provides professionals with a tool to pursue their professional objectives and legitimacy for their professional steps with patients, resulting in a lack of equality from which different perspectives can be examined side by side. However, with shared decision-making, patients are expected to be engaged and to help determine the course of treatment; the language surrounding classifications and symptoms does not currently allow that to happen sufficiently.

Figure 1 DSM as a professional tool, adapted from Figure 1, ‘Design of a Digital Comic Creator (It’s Me) to Facilitate Social Skills Training for Children With Autism Spectrum Disorder: Design Research Approach’, by Terlouw et al., CC-BY ( 65 ).

This is where boundary objects come into play. The focused shaping of boundary objects can ensure a more equal role for different stakeholders ( 65 – 67 ). Boundary objects can also trigger perspective-making and -taking from a reflective dialogical learning mechanism ( 68 – 70 ), which ensures a better shared understanding of all perspectives. Boundary objects and their dialogical learning mechanisms also align well with co-design ( 71 ). If we consider the DSM a boundary object, it positions itself between the activity system of the professionals, patients, and other people close to the patient ( Figure 2 ). The boundary between activity systems represents not only the cultural differences and potential challenges in actions and interactions but also the significant value in establishing communication and collaboration ( 71 ). All sides can give meaning to the DSM language from their perspective. By effectively considering the DSM as a boundary object, the DSM serves as a conversation piece—a product that elicits and provides room for questions and comments from other people, literally one that encourages conversation ( 72 ). As a conversation piece rather than a determinative classification system, it can contribute to mapping the meaning of complaints, behaviors, signs, and patterns for different invested parties. It also provides space for the patient’s contextual factors, subjective experience, needs, and life events, which are essential to giving constructive meaning to mental distress. This allows for interpretative flexibility; professionals can structure their work, while patients can give meaning to their subjective experience of mental distress.

Figure 2 DSM as a boundary object, adapted from Figure 1, ‘Design of a Digital Comic Creator (It’s Me) to Facilitate Social Skills Training for Children With Autism Spectrum Disorder: Design Research Approach’, by Terlouw et al., CC-BY ( 65 ).

As the DSM as a boundary object enables interpretative flexibility, it could then be used to enact conversations and develop a shared understanding in partnership between the patient and the professional; patients are no longer ‘diagnosed’ with a disorder from a professional point of view. It is important to note that the conceptual history of understanding the diagnostic process as essentially dialogical and not as a merely technical-quantitative procedure was already started in the early 1900s. For example, in the 1913 released ‘General Psychopathology,’ Karl Jaspers presented a phenomenological and comprehensive perspective for psychiatry with suggestions about how to understand the psychopathological phenomena as experienced by the patient through empathic understanding, allowing to understand the patient’s worldview and existential meanings ( 73 ). A century after its first publication, academics continue to leverage Jaspers’ ideas to critique modern operationalist epistemology ( 74 ). Following the notion of the diagnostic process as a dialogical one, the reconsideration of the DSM as a boundary object could accommodate the patient’s idiographic experience and the professional’s knowledge about mental distress by using these potential spectra as conversation pieces, shifting the power balance in clinical practice towards co-creation and dialogue. The spectra can then be explained as umbrella terms that indicate a collection of frequently occurring patterns and signs that can function as a starting point for a co-creative inquiry that promotes dialogue, aligning more with current empirical evidence of lived experience than using classifications as diagnoses.

Considering the advantages and strengths boundary objects bring to a mental health care system centered around shared decision-making and co-creation, the DSM could be a boundary object that is interpreted from various perspectives. Take, for example, altered perceptions, which is a characteristic commonly seen in people who receive a psychosis-related classification in clinical care. For some, these perceptions have person-specific meaning ( 75 , 76 ). By using the DSM as a boundary object and as a conversation piece, the patient and professional can give meaning by using the spectra in the manual as a starting point for a common language instead of using a classification to explain the distress. This requires a phenomenological and idiographic approach considering person-specific meaning and idiosyncrasies. Consequently, diagnostic practices should be iterative to align with the dynamic circumstances, with the individual’s narrative taking center stage in co-creation between professional and patient ( 41 , 49 ), as this reconsidered role fosters the engagement instead of the disengagement of patients. Additionally, the potential role of the DSM as a boundary object and conversation piece may also have a positive effect on societal and scientific levels, specifically on how mental distress is perceived and conceptualized. It can ‘systemically contextualize’ mental distress, which could eliminate the disorderism and the psychiatrization of society, and in the end, hopefully, contribute to population salutogenesis.

3 Co-design of DSM spectra of mental distress

If the DSM is reconsidered as a conversation piece in which spectra of mental distress replace classifications, it is important to address that these must be co-designed to accommodate diverse stakeholder perspectives and various types of knowledge side by side in clinical practice. Therefore, developers and designers need to embrace lived experience in the co-development of these spectra of mental distress to ensure patients’ engagement in clinical practice, as the patient effectively becomes a stakeholder of the DSM. This requires a different approach and procedure than DSM task forces used in past iterations. In this section, we will address our second hypothesis: embracing design approaches in redesigning the DSM to a conversation piece that uses spectra of mental distress instead of classifications will stimulate the integration of diverse perspectives and voices in reshaping mental health care. While we focus a little on the what (spectra of mental distress), we mainly focus on the how (the procedure that could be followed to arrive at the what). First, we will discuss the importance of lived experience leadership in design and research. Second, we argue that in the conceptual co-design of DSM spectra, lived experience leadership can be a way forward. Third, we take the stance that a designerly way of thinking and doing can shift the premature overcommitment task forces had to iterative exploration. In the concluding paragraph, we propose a design procedure that embraces engagement and iteration as core values for developing robust and flexible spectra of mental distress that are meaningful for service users and professionals.

3.1 Lived experience leadership and initiatives in design and research

First, let us briefly examine the evolution of lived experience in design and science over time to provide context for why engaging people with lived experience in the design of spectra of mental distress is important for innovation. Since 1960, people with lived experiences have tried to let their voices be heard, but initially to no avail, and their civil rights movement of reformist psychiatry was labeled as ‘anti-psychiatry’ ( 77 ). During the turn of the millennium, lived experience received increased recognition and eventually became an important pillar of knowledge that informed practice and continues to do so on various levels of mental health care ( 34 , 36 , 78 – 81 ). While there is currently growing attention to the perspective of lived experience in, for example, mental health research ( 79 , 80 , 82 , 83 ) and mental health care design and innovation ( 84 – 90 ), overall, their involvement remains too low in the majority of research and design projects ( 88 , 91 , 92 ). While there has been a significant increase in the annual publication of articles claiming to employ collaborative methods with people with lived experience, these studies often use vague terms to suggest a higher engagement level than is the case ( 93 ). This has led to initiatives such as that of The Lancet Psychiatry to facilitate transparent reporting of lived experience work ( 93 , 94 ).

Although the involvement of people with lived experience and its reporting needs attention in order to prevent tokenism and co-optation ( 89 ), some great user-driven initiatives resulted in innovative design and research that improved mental health care and exemplifies why their engagement should be mandatory. The Co-Design Living Labs is such an initiative. Its program exemplifies an adaptive and embedded approach for people with lived experiences of mental distress to drive mental health research design to translation ( 95 ). In this community-based approach, people with lived experience, their caregivers, family members, and support networks collaboratively drive research with university researchers, which is very innovative considering the relatively low engagement of people with lived experience in general mental health research. Another example is the development of person-specific tapering medication initiated by people with lived experience of withdrawal symptoms. People with lived experience began to devise practical methods to discontinue medications on their own safely because of the lack of a systematic and professional response to severe and persistent withdrawal. This resulted in the accumulation of experience-based knowledge about withdrawal, ultimately leading to co-creating what is now known as tapering strips ( 81 ). The development of these tapering strips shows that people with lived experience have novel experience-based ideas for design and research that can result in human-centered innovation. Both examples underline the importance of human-centered design in which people with lived experience and knowledge are taken seriously and why the participation era requires that individuals with lived experience are decision-makers from the project’s start to produce novel perspectives for innovative design and research ( 88 , 93 ).

3.2 The conceptual co-design of DSM spectra of mental distress and the potential of integrating lived experiences

Engaging people with lived experience of mental distress in redesigning the DSM towards a spectrum-based guideline is of special importance, albeit a more conceptual design task in comparison to the earlier examples. What mental distress is remains a fundamental philosophical and ontological question that should be addressed in partnership as it sits at the core of how mental health care is organized. To allow novel ontologies to reach their full potential and act as drivers of a landscape of promising innovative scientific and clinical approaches, investment is required in development and elaboration ( 30 ). This, as well as the epistemic pluralism among theoretical models on mental health problems ( 31 ), makes it evident there is currently not one coherent accepted explanation or consensus on what mental distress is and how it exists. Without clear etiological understanding, the most logical first step should be to involve people with lived experience of mental distress in the redevelopment of the DSM. Accounts from people with lived experience of mental distress are directly relevant to the design of the DSM, as they provide a more comprehensive and accurate understanding of mental distress and its treatment ( 96 ). Moreover, the DSM’s conceptualization as a major determinative classification system could be standing at the core of psychiatry’s “identity crisis”, where checklists of symptoms replaced thoughtful diagnoses despite after decades of brain research, no biomarker has been established for any disorders defined in the DSM ( 10 , 97 ).

Design approaches can help DSM task forces prioritize integrating lived experiences to co-create a framework that can accommodate a range of perspectives to make it viable as a conversation piece. As DSM classifications do not reflect reality ( 98 ), listening to people with firsthand experiences is necessary. The CHIME framework – a conceptual framework of people’s experiences of recovery – shows, for example, a clear need to diagnose not solely based on symptoms but also considering people’s stages in their journey of personal recovery ( 80 ). Further, bottom-up research shows that the lived experience perspective of psychosis can seem very different compared to conventional psychiatric conceptualizations ( 82 ). This is also the case for the lived experience of depression ( 99 ). Design approaches can ensure that such much-needed perspectives and voices are adhered to in developing meaningful innovations ( 88 ), which brings us back to the design of the DSM. Although the DSM aims to conceptualize the reality of mental distress, engaging people with experiences of living with mental distress has never been prioritized by the DSM task force as an important epistemic resource. This is evidenced by the historically low engagement of people with lived experiences and their contexts. For example, although “individuals with mental disorders and families of individuals with mental disorders” participated in providing feedback in the DSM-5 revisions process ( 14 ), when and how they were involved, what feedback they gave, and how this was incorporated are not described. According to the Involvement Matrix ( 100 ) — a matrix that can be used to assess the contribution of patients in research and design —, giving feedback can be classified as ‘listeners’ or ‘co-thinkers,’ which are both low-involvement roles. Moreover, a review of the members of the DSM task forces and working groups listed in the introductions of the DSMs shows patients have never been part of the DSM task force and thus never been part of the decision-making process ( 96 ). Human-centered design is difficult to achieve when people with lived experience are not involved from preparation to implementation but are only asked to give feedback on expert consensus ( 88 ).

In the participation era, using a design approach in mental health care without engaging important stakeholders can be problematic. For example, it is evident that the involvement of people with lived experience changes the nature of an intervention dramatically, as people’s unique first-hand experiences, insights about mental states, and individual meaning and needs are often different in design activities as opposed to what general scientific and web-related resources suggest ( 101 , 102 ). Further, clear differences are reported around designers, researchers, and clinicians on one side and service user ideas of meaningful interventions on the other ( 102 , 103 ). Thus, the meaningful engagement of people with lived experience in design processes always exposes gaps between general research and the interests and lives of service users ( 104 ). This makes the participation of people with lived experience in developing innovative concepts — and, as such, in the conceptual design of DSM spectra of mental distress — essential because their absence in design processes may lead to ineffective outcomes ( 102 ). This design perspective may explain some of the negative effects of the DSM. The classifications aimed to be empirical constructs reflecting reality, yet phenomena such as reification and the classificatory looping effect emerged ( 42 , 51 ). From a design perspective, the emergence of these effects may have a simpler explanation than previously presumed: the premature over-commitment in the DSM’s design processes without input from individuals with firsthand experiences.

3.3 Shifting the premature over-commitment to iterative exploration

The centrist development approach used to design the DSM implicitly frames people with mental distress as ‘ordinary people,’ resulting in ‘average solutions’ because their experiences are decontextualized and lumped together on a group level — eventually leading to general descriptions for a universal appliance. Instead, a more human-centered iterative design process in which people with lived experience play an important role, preferably as decision-makers, can promote the design of spectra of mental distress that leave room for idiosyncrasies that correspond with people’s living environments on an individual level. This can potentially ensure that they are actually helpful for shared decision-making between patients and professionals and resonate in person-centered mental health care. A design approach is feasible for this aim because design processes are not searching for a singular ‘truth’ but rather exploring the multiple ‘truths’ that may be relevant in different contexts ( 105 ). This can be of added value to conceptualizing spectra of mental distress, which is known to have characteristics that overlap between people but also to have a unique phenomenology and contextual foundation for each individual — in the case of mental distress, there literally are multiple truths dependable on who and what you ask in what time and place. Furthermore, design approaches enable exploration and discovery ( 106 ). Designers consistently draw cues from the environment and introduce new variables into the same environment to eventually discover what does and does not work ( 107 ). This explorative attitude also ensures the discovery of unique insights, such as people’s experiential knowledge and contexts. Therefore, from a design perspective, predetermining solutions might be ineffective for arriving at DSM innovation. This is, for example, aptly described by Owens et al. ( 101 ):

“… the iterative nature of the participatory process meant that, although a preliminary programme for the whole workshop series was drawn up at the outset, plans had to be revised in response to the findings from each session. The whole process required flexibility, a constantly open mind and a willingness to embrace the unexpected”.

These insights illustrate the core of design that can guide the development of future DSM iterations: design enables the task force to learn about mental health problems without an omniscient perspective by iteratively developing and testing conceptualizations in the environment in partnership with the target group. As participatory design studies consistently demonstrate, solutions cannot be predetermined solely based on research and resources. The involvement of individuals with lived experience and their contexts invariably uncovers crucial serendipitous insights that challenge the perspectives on the problem. This can expose important misconceptions, such as the tendency to underestimate the complexity of human experience and decontextualize it from its environment.

3.4 Insights that could inform a procedure for co-designing spectra of mental distress

People with lived experience need to be highly involved in developing meaningful spectra of mental distress to guide conversations in clinical practice. As we now have a comprehensive understanding of what design approaches can offer to the development procedure of a lived experience-informed DSM, we will highlight these insights in this paragraph.

3.4.1 Balance academic research with lived experience insights

In the design procedure of a future DSM, academic research can be used to learn about people’s experiences of mental distress but never as the source alone for the development of spectra of mental distress. In this way, designers and researchers in mental health care need to involve people with lived experience at the heart of design processes as partners and come to unique insights together without an omniscient perspective. The aim should not be to design general descriptions but to design spectra that are flexible enough to adapt to local needs and constraints for the various parties using them yet robust enough to maintain a common identity across different locations. This allows the DSM to have different meanings in different social worlds, while at the same time, their structure is common enough for more than one world to recognize them.

3.4.2 Prevent premature overcommitment in the design process

Conceptualizations of spectra of mental distress must not be predetermined, and there should be no overcommitment to concepts in the early phases of the project. Thus, the task force should avoid viewing mental distress too narrowly, too early on in the process. This enables the evolution of lived experience-based spectra in an iterative design- and test process. The starting point should be an open representation of mental distress and discover together with people with lived experience how this could be best conceptualized and what language should be used. This allows room for exploring and discovering what works and aligns with patients’ needs and experiences in their living environments and professionals’ needs in their work environments.

3.4.3 Designing and testing is also a form of research

Researchers and designers should realize that designing and testing conceptualizations in partnership with people with lived experience also results in unique knowledge that can guide the development — designing and testing the developed concepts is a form of research. For example, exploring if a certain designed spectrum resonates as a conversation piece between patients and professionals in clinical practice provides qualitative insights that cannot be predicted beforehand. In this way, science and design can complement the innovation of the DSM: science benefits from a design approach, while design benefits from scientific methods ( 108 ). Flexible navigation between design and science would indicate that the developed DSM can be meaningful as a conversation piece in clinical practice.

3.4.4 Good design comes before effective science

Good design comes before effective science, as innovations are useless if not used, even if they are validated by science ( 85 ). Although the development of the DSM is often described as a scientific process, our analysis indicates that it is more accurately described as a design process. As a design process, it requires a methodologically sound design approach that is suitable for involving patients and people with lived experience. Co-design is a great contender for this purpose, as a systematic review showed this approach had the highest level of participant involvement in mental health care innovation ( 89 ). Although people with lived experience have never been involved as decision-makers, this should be the aim of the design process of a novel DSM in the participation era. This promotes lived experience leadership in design and, ultimately, contributes to more effective science.

3.4.5 Avoid tokenism and co-optation

Involving people with lived experience as decision-makers in redesigning the DSM must avoid tokenism and co-optation and address power imbalances. The first step that the task force can take is to use the Involvement Matrix ( 100 ) together with people with lived experiences to systematically and transparently plan, reflect, and report on everyone’s contribution to the design process. This has not been prioritized in the past DSM revisions. In the end, transparency and honesty about collaboration can support the empowerment of people with firsthand perspectives and shift the power imbalance towards co-creation for more human-centered mental health care. This is needed, as the involvement of people with lived experience in design and research processes is currently too low and obscured by vague terms and bad reporting.

4 Discussion and conclusion

In this hypothesis and theory paper, we have argued that the current role of the DSM, as an operating manual for professionals, can be reconsidered as a boundary object and conversation piece for patients and professionals in clinical practice that stimulates dialogue about mental distress. In this discussion, we will address five themes. First, while we argued that research acknowledges the absence of empirical support for biological causation, we believe characterizing the DSM as entirely non-empirical may be incorrect. Second, we discuss our perspective on balancing between a too-narrow medical perspective and a too-broadly individualized perspective. Third, we discuss why mental health care also needs novel methods for inquiry if the DSM is reconsidered as a conversation piece. Fourth, we discuss that while we are certain that design approaches can be fruitful for redesigning the DSM, some challenges regarding tokenism, co-optation must be addressed. We conclude by examining various methodological challenges and offering recommendations for the co-design process of the DSM.

4.1 Redesigning instead of discarding the DSM

The DSM is too deeply entrenched in mental health care to discard it simply. The DSM is embedded in not only mental health care but also society. For instance, a DSM classification is necessary in the Netherlands to get mental health care reimbursement, qualify for additional education test time, or receive subsidized assisted living. Moreover, it is ingrained in research and healthcare funding, making it unproductive and somewhat dangerous to discard without an alternative, as it may jeopardize access to care and impact insurance coverage for treatment and services that people with mental distress need. Therefore, we posited that instead of discarding the DSM, its role should be reconsidered in a mental health care system centered around shared decision-making and co-creation to eliminate pervasive effects such as the disengagement of patients, reification, disorderism, and the psychiatrization of society. However, the DSM categories are not entirely a priori constructed as is sometimes claimed, as the psychiatric symptom space and diagnostic categories took shape in the late nineteenth century through decades of observation ( 109 ).

While this adds important nuance to the idea that the design of the DSM is entirely non-empirical, it does not invalidate the argument that the DSM design is grounded in a potentially false ontology ( 64 ). Though the lack of evidence does not necessarily indicate evidence of absence, and the biological context in some way plays a role, research shows various other dimensions of life — including the social, historical, relational, environmental, and more — also influence mental distress, yet are significantly underemphasized in its current design. We believe that we showed this manifests itself most prominently in the various highly arbitrary classification designs that can confuse the professional and the patient and appear limited in providing meaningful guidance for clinical practice, design, and research. That is why we have proposed redesigning the next iteration of the DSM to primarily focus on formulating a set of spectra of distress. Reconsidering the DSM leverages one of its biggest strengths: the DSM is not bound by an analytic procedure but rather is guided by scientific debate ( 17 ). Further, developments and amendments to psychiatric classification systems have always reflected wider social and cultural developments ( 110 ). The recognition, implementation, and impact of the DSM in Western countries can even be seen as a reason not to focus on developing alternative models but rather to redesign the DSM so that it conceptually aligns with the social developments, scientific findings, and needs of people in the 21st century, as it is already deeply embedded in systems. Given that DSM classifications are now recognized as inaccurate depictions of the reality of mental distress ( 98 ) and that, at the same time, mental health care is shifting towards person-centeredness and shared decision-making, we believe the proposals in this article are not radical but rather the most meaningful way forward to accommodate diverse perspectives.

4.2 Balancing between a too-narrow medical categorization and a too-broadly individualized approach

From a classical psychopathological perspective, integrating the lived experiences of those with mental distress into the redevelopment of the DSM as a boundary object presents certain conceptual challenges. For example, uncritically overemphasizing individual experiences might lead to an underappreciation of psychopathological manifestations like, for example, altered perceptions. Conversely, excluding people with lived experience from the DSM’s design processes has resulted in its own conceptual and epistemic issues, such as undervaluing the idiographic, contextual, and phenomenological aspects of individual mental distress. Therefore, we argue that achieving a balance between these differing but crucial perspectives should result from a co-design procedure for a revised DSM. Determining this balance before obtaining results from such a process is too premature and arbitrary and would contradict our recommendation to prevent over-commitment in the early stages of the design process. As people with lived experience were never previously involved, it is impossible to predict the outcomes of a co-design procedure or hypothesize about a clear distinction between these perspectives in the DSM’s conceptual development beforehand. As seen in past iterations, prematurely drawing rigid lines could hinder the design process and result in design fixation. From the perspective of boundary objects, the DSM cannot have one dominant perspective if it is to function effectively. All stakeholders must be able to give meaning to the spectra of mental distress from their own activity systems, and these perspectives should be equal in order to create a shared awareness of the different perspectives involved. A DSM designed as a boundary object triggers dialogical learning mechanisms, ensuring the multiple perspectives are harmonized rather than adjusted to fit one another, ensuring no single perspective prevails over the others or consensus is pursued ( 71 , 111 ).

4.3 Novel methods for inquiry to accompany the reconsidered role of the DSM

If the DSM is reconsidered and designed as a conversation piece and classifications are replaced by spectra, in clinical practice, a unique language needs to be co-developed between the patient and the professional, and an equal relationship is important to ally. For example, if we consider the person-specific meanings of altered perceptions, they need to be explored, as they have clinical relevance. However, for such purposes, current diagnostic methods in clinical practice are limiting because they are highly linguistic and tailored to classification systems and the needs and praxis of the professionals. This can impede the DSM’s effectiveness as a tool for dialogue. Expressing the uniqueness of an experience of mental distress is difficult — especially during a mental crisis — let alone effectively communicating it to a professional. While people with mental distress can effectively communicate their behaviors and complaints, which fits the current use of the DSM, people have far more embodied and experiential knowledge of their distress. How people cope with their mental distress in the contexts they are living in is very difficult to put into words without first making these personal and contextual insights tangible ( 41 ), yet this is essential information for when the DSM is used as a boundary object and conversation piece. To accommodate the patient in making this knowledge tangible, the professional becomes more of a facilitator than an expert, emphasizing therapeutic relationships and the healing effects of ritualized care interactions ( 39 ). This transformation requires novel co-creative methods for inquiry ( 41 ) and professional training ( 39 ). Therefore, expanding the diagnostic toolkit with innovative and creative tools and embracing professionals such as art therapists, social workers, and advanced nurse practitioners to enable and support patients to convey their narratives and needs in their own way is essential if the DSM is to be used as a boundary object and conversation piece.

4.4 Promoting lived experience leadership in the co-design procedure

Despite longstanding calls for the APA to include people with lived experience in the decision-making processes for diagnostic criteria, the DSM-5 task force did not accept this inclusion. The task force believed incorporating these perspectives could compromise objectivity in the scientific process ( 96 ). This mindset ensures that research, design, and practice remain predominantly shaped by academics and professionals, causing conventional mental health care to perpetuate itself. It continues to repeat the same approaches and consequently achieves the same results. Therefore, people with lived experience should have more influence in the participation era to accelerate change in mental health care. This proposition comes with some challenges regarding power imbalances that need addressing. While it is acknowledged that the involvement of individuals with lived experience yields unique insights and can serve as strong collaborators and knowledgeable contributors, they are never given decision-making authority in design processes in mental health care ( 88 , 89 , 92 ) or in the DSM’s development processes ( 96 ). This lack of authority impedes lived experience leadership ( 91 , 112 ) and subsequently stands in the way of effectively reconsidering and redesigning the DSM. To avoid tokenism, the DSM revision process should not settle for low engagement and involvement but set the bar higher by redressing power imbalances ( 113 ). Furthermore, in the co-design process of the DSM, the task force should not view objectivity as the opposite of subjectivity or strive for consensus. Instead, they should value group discussions and disagreements, encouraging stakeholders to debate and explore the sources of their differing perspectives and knowledge ( 96 ). Shifting towards lived experience leadership starts with perceiving and engaging people with lived experiences of mental distress as experts of their experiences in iterative design and research processes and giving them this role in revising the DSM.

4.5 Methodological considerations for a co-design procedure of the DSM

Merely positioning people with lived experience as partners and decision-makers is insufficient; there are also significant methodological concerns regarding the execution of design research in mental health care. Although iteration and participation are essential for design in mental health care, as designers focus on the unmet needs of service users and ways to improve care ( 114 ), research shows design is not always executed iteratively, and end users are not always involved. For example, about one-third of projects that designed mental health interventions did not adopt an iterative process ( 85 ). The engagement of end users in design processes in mental health is also not yet a common practice. For instance, a systematic review of serious games in mental health for anxiety and depression found that only half of these games, even while reporting using a participatory approach, were designed with input from the intended end-users ( 115 ). A systematic review of design processes that aimed to design innovations for people with psychotic symptoms overlaps these findings, as less than half of the studies demonstrated a high level of participant involvement in their design processes ( 89 ).

The low level of involvement and lack of iterative approaches in mental health care design offer valuable insights for future processes. If the DSM task force aims to adopt a co-design approach, it should incorporate these lessons to enhance design effectiveness. First, the task force must understand that design has a different aim, culture, and methods than the sciences ( 116 ). The scientific approach typically implies investigating the natural world through controlled experiments, classifications, and analysis, emphasizing objectivity, rationality, neutrality, and a commitment to truth. In contrast, a design approach focuses on studying the artificial world, employing methods such as modeling, pattern formation, and synthesis, guided by core values of practicality, ingenuity, empathy, and concern for appropriateness. Second, the task force should consider the known challenges they will encounter and need to navigate to let the paradigms be complementary in practice ( 117 ). Further, the task force should consider that the nature of design is exploratory, iterative, uncertain, and a social form of inquiry and synthesis that is never perfect and never quite finished ( 84 ). This requires tolerating ambiguity and having trust ( 101 ). Lastly, more transparency in the participatory work of the task force is called for, beginning with being honest, being detailed, addressing power imbalances, being participatory in reporting the participatory approach, and being excited and enthusiastic about going beyond tokenistic engagement ( 118 ).

Despite these challenges, transforming psychiatric diagnoses by reconsidering and redesigning the DSM as a boundary object and conversation piece could be a step in the right direction. This would shift the power balance towards shared ownership in a participation era that fosters dialogue instead of diagnosis. We hope this hypothesis and theory paper can give decisive impulses to the much-needed debate on and development of psychiatric diagnoses and, in the end, contribute to lived experience-informed psychiatric epistemology. Furthermore, as a product of an equal co-production process between various disciplines and types of knowledge, this paper shows it is possible to harmonize perspectives on a controversial topic such as the DSM.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author/s.

Author contributions

LV: Conceptualization, Methodology, Project administration, Writing – original draft, Writing – review & editing. GT: Conceptualization, Methodology, Visualization, Writing – original draft, Writing – review & editing. JVO: Conceptualization, Writing – original draft, Writing – review & editing. SM: Conceptualization, Writing – original draft, Writing – review & editing. JV: Writing – original draft, Writing – review & editing. NB: Writing – original draft, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. We appreciate the financial support of the FAITH Research Consortium, GGZ-VS University of Applied Science, as well as from the NHL Stenden University of Applied Sciences PhD program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Acknowledgments

We thank the reviewers for their thorough reading of our manuscript and valuable comments, which improved the quality of our hypothesis and theory paper.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

1. Fischer BA. A review of american psychiatry through its diagnoses. J Nervous Ment Dis . (2012) 200:1022–30. doi: 10.1097/NMD.0b013e318275cf19

CrossRef Full Text | Google Scholar

2. Grob GN. Origins of DSM-I: a study in appearance and reality. Am J Psychiatry . (1991) 148:421–31. doi: 10.1176/ajp.148.4.421

PubMed Abstract | CrossRef Full Text | Google Scholar

3. Brendel DH. Healing psychiatry: A pragmatic approach to bridging the science/humanism divide. Harvard Rev Psychiatry . (2004) 12:150–7. doi: 10.1080/10673220490472409

4. Braslow JT. Therapeutics and the history of psychiatry. Bull History Med . (2000) 74:794–802. doi: 10.1353/bhm.2000.0161

5. Kawa S, Giordano J. A brief historicity of the Diagnostic and Statistical Manual of Mental Disorders: Issues and implications for the future of psychiatric canon and practice. Philosophy Ethics Humanities Med . (2012) 7:2. doi: 10.1186/1747-5341-7-2

6. Mayes R, Horwitz AV. DSM-III and the revolution in the classification of mental illness. J History Behav Sci . (2005) 41:249–67. doi: 10.1002/(ISSN)1520-6696

7. American Psychiatric Association. Diagnostic & Statistical manual of Mental Disorders . 3rd edition. Washington, D.C: American Psychiatric Association (1980).

Google Scholar

8. Gambardella A. Science and innovation: the US pharmaceutical industry during the 1980s . New York: Cambridge University Press (1995). doi: 10.1017/CBO9780511522031

9. Klerman GL, Vaillant GE, Spitzer RL, Michels R. A debate on DSM-III. Am J Psychiatry . (1984) 141:539–53. doi: 10.1176/ajp.141.4.539

10. Scull A. American psychiatry in the new millennium: a critical appraisal. psychol Med . (2021) 51:1–9. doi: 10.1017/S0033291721001975

11. American Psychiatric Association. Diagnostic and statistical manual of mental disorders . 4th edition. Washington, D.C: American Psychiatric Association (1994).

12. Shaffer D. A participant’s observations: preparing DSM-IV. Can J Psychiatry . (1996) 41:325–9. doi: 10.1177/070674379604100602

13. Follette WC, Houts AC. Models of scientific progress and the role of theory in taxonomy development: A case study of the DSM. J Consulting Clin Psychol . (1996) 64:1120–32. doi: 10.1037//0022-006X.64.6.1120

14. American Psychiatric Association. Diagnostic and statistical manual of mental disorders: DSM-5-TR . 5th ed. Washington, Dc: American Psychiatric Association Publishing (2022). doi: 10.1176/appi.books.9780890425787

15. Pickersgill MD. Debating DSM-5: diagnosis and the sociology of critique. J Med Ethics . (2013) 40:521–5. doi: 10.1136/medethics-2013-101762

16. Hyman SE. The diagnosis of mental disorders: the problem of reification. Annu Rev Clin Psychol . (2010) 6:155–79. doi: 10.1146/annurev.clinpsy.3.022806.091532

17. Zachar P, Kendler KS. Psychiatric disorders: A conceptual taxonomy. Am J Psychiatry . (2007) 164:557–65. doi: 10.1176/ajp.2007.164.4.557

18. Francis A, First MB, Pincus HA. DSM-IV Guidebook . Washington DC: American Psychiatric Association (1995).

19. Spitzer RL, Williams JBW, Endicott J. Standards for DSM-5 reliability. Am J Psychiatry . (2012) 169:537–7. doi: 10.1176/appi.ajp.2012.12010083

20. Gómez-Carrillo A, Kirmayer LJ, Aggarwal NK, Bhui KS, Fung KP-L, Kohrt BA, et al. Integrating neuroscience in psychiatry: a cultural–ecosocial systemic approach. Lancet Psychiatry . (2023) 10:296–304. doi: 10.1016/s2215-0366(23)00006-8

21. Morgan C, Charalambides M, Hutchinson G, Murray RM. Migration, ethnicity, and psychosis: toward a sociodevelopmental model. Schizophr Bull . (2010) 36:655–64. doi: 10.1093/schbul/sbq051

22. Howes OD, Murray RM. Schizophrenia: an integrated sociodevelopmental-cognitive model. Lancet . (2014) 383:1677–87. doi: 10.1016/S0140-6736(13)62036-X

23. Alegría M, NeMoyer A, Falgàs Bagué I, Wang Y, Alvarez K. Social determinants of mental health: where we are and where we need to go. Curr Psychiatry Rep . (2018) 20. doi: 10.1007/s11920-018-0969-9

24. Jeste DV, Pender VB. Social determinants of mental health. JAMA Psychiatry . (2022) 79:283–4. doi: 10.1001/jamapsychiatry.2021.4385

25. Huggard L, Murphy R, O’Connor C, Nearchou F. The social determinants of mental illness: A rapid review of systematic reviews. Issues Ment Health Nurs . (2023) 44:1–11. doi: 10.1080/01612840.2023.2186124

26. Kirkbride JB, Anglin DM, Colman I, Dykxhoorn J, Jones PB, Patalay P, et al. The social determinants of mental health and disorder: evidence, prevention and recommendations. World psychiatry: Off J World Psychiatr Assoc (WPA) . (2024) 23:58–90. doi: 10.1002/wps.21160

27. Alon N, Macrynikola N, Jester DJ, Keshavan M, Reynolds ICF, Saxena S, et al. Social determinants of mental health in major depressive disorder: umbrella review of 26 meta-analyses and systematic reviews. Psychiatry Res . (2024) 335:115854. doi: 10.1016/j.psychres.2024.115854

28. Read J, Bentall RP. Negative childhood experiences and mental health: theoretical, clinical and primary prevention implications. Br J Psychiatry . (2012) 200:89–91. doi: 10.1192/bjp.bp.111.096727

29. van Os J, Kenis G, Rutten BPF. The environment and schizophrenia. Nature . (2010) 468:203–12. doi: 10.1038/nature09563

30. Köhne A, de Graauw LP, Leenhouts-van der Maas R, van Os J. Clinician and patient perspectives on the ontology of mental disorder: a qualitative study. Front Psychiatry . (2023) 14:1081925. doi: 10.3389/fpsyt.2023.1081925

31. Richter D, Dixon J. Models of mental health problems: a quasi-systematic review of theoretical approaches. J Ment Health . (2022) 32:1–11. doi: 10.1080/09638237.2021.2022638

32. World Health Organization. Guidance on community mental health services: Promoting person-centred and rights-based approaches (2021). Available online at: https://www.who.int/publications/i/item/9789240025707 .

33. Huber M, Knottnerus JA, Green L, Horst HVD, Jadad AR, Kromhout D, et al. How should we define health? BMJ . (2011) 343:d4163–3. doi: 10.1136/bmj.d4163

34. Davidson L. The recovery movement: implications for mental health care and enabling people to participate fully in life. Health Affairs . (2016) 35:1091–7. doi: 10.1377/hlthaff.2016.0153

35. Dixon LB, Holoshitz Y, Nossel I. Treatment engagement of individuals experiencing mental illness: review and update. World Psychiatry . (2016) 15:13–20. doi: 10.1002/wps.20306

36. Karbouniaris S. Let’s tango! Integrating professionals’ lived experience in the tranformation of mental health services. (2023). Available online at: https://hdl.handle.net/1887/3640655 .

PubMed Abstract | Google Scholar

37. Elwyn G, Laitner S, Coulter A, Walker E, Watson P, Thomson R. Implementing shared decision making in the NHS. BMJ . (2010) 341:c5146–6. doi: 10.1136/bmj.c5146

38. Oueslati R, Woudstra AJ, Alkirawan R, Reis R, Zaalen Yv, Slager MT, et al. What value structure underlies shared decision making? A qualitative synthesis of models of shared decision making. Patient Educ Couns . (2024) 124:108284. doi: 10.1016/j.pec.2024.108284

39. van Os J, Guloksuz S, Vijn TW, Hafkenscheid A, Delespaul P. The evidence-based group-level symptom-reduction model as the organizing principle for mental health care: time for change? World Psychiatry . (2019) 18:88–96. doi: 10.1002/wps.20609

40. Meerman S. ADHD and the power of generalization: exploring the faces of reification. researchrugnl . (2019). doi: 10.33612/diss.84379221

41. Veldmeijer L, Terlouw G, van ‘t VJ, van Os J, Boonstra N. Design for mental health: can design promote human-centred diagnostics? Design Health . (2023) 7:1–19. doi: 10.1080/24735132.2023.2171223

42. te Meerman S, Freedman JE, Batstra L. ADHD and reification: Four ways a psychiatric construct is portrayed as a disease. Front Psychiatry . (2022) 13:1055328. doi: 10.3389/fpsyt.2022.1055328

43. Guloksuz S, van Os J. The slow death of the concept of schizophrenia and the painful birth of the psychosis spectrum. psychol Med . (2017) 48:229–44. doi: 10.1017/S0033291717001775

44. Os Jv, Scheepers F, Milo M, Ockeloen G, Guloksuz S, Delespaul P. It has to be better, otherwise we will get stuck.” A Review of Novel Directions for Mental Health Reform and Introducing Pilot Work in the Netherlands. Clin Pract Epidemiol Ment Health . (2023) 19. doi: 10.2174/0117450179271206231114064736

45. Bowker GC. Susan Leigh Star. Sorting Things Out: Classification and Its Consequences (Inside technology) . Cambridge, MA: Mit Press (1999). doi: 10.7551/mitpress/6352.001.0001

46. Perkins A, Ridler J, Browes D, Peryer G, Notley C, Hackmann C. Experiencing mental health diagnosis: a systematic review of service user, clinician, and carer perspectives across clinical settings. Lancet Psychiatry . (2018) 5:747–64. doi: 10.1016/S2215-0366(18)30095-6

47. Ben-Zeev D, Young MA, Corrigan PW. DSM-V and the stigma of mental illness. J Ment Health . (2010) 19:318–27. doi: 10.3109/09638237.2010.492484

48. Thornicroft G. Shunned: discrimination against people with mental illness . Oxford: Oxford University Press (2009).

49. Köhne ACJ. The ontological status of a psychiatric diagnosis: the case of neurasthenia. Philosophy Psychiatry Psychol . (2019) 26:E–1-E-11. doi: 10.1353/ppp.2019.0008

50. Hacking I. Kinds of people: moving targets. Proceedings of the British Academy . Oxford: Oxford University Press Inc (2007). doi: 10.5871/bacad/9780197264249.003.0010

51. Hacking I. The social construction of what? Harvard: Harvard University Press (1999).

52. Wienen AW, Sluiter MN, Thoutenhoofd E, de Jonge P, Batstra L. The advantages of an ADHD classification from the perspective of teachers. Eur J Special Needs Educ . (2019) 34:1–14. doi: 10.1080/08856257.2019.1580838

53. Franz DJ, Richter T, Lenhard W, Marx P, Stein R, Ratz C. The influence of diagnostic labels on the evaluation of students: a multilevel meta-analysis. Educ Psychol Rev . (2023) 35. doi: 10.1007/s10648-023-09716-6

54. Köhne ACJ. In search of a better ontology of mental disorder. (2022). doi: 10.33540/1591

55. de Ridder B, van Hulst BM. Disorderism: what it is and why it’s a problem. Tijdschr Psychiatr . (2023) 65:163–6.

56. Kazda L, Bell K, Thomas R, McGeechan K, Sims R, Barratt A. Overdiagnosis of Attention-Deficit/Hyperactivity Disorder in children and adolescents. JAMA Network Open . (2021) 4. doi: 10.1001/jamanetworkopen.2021.5335

57. Beeker T, Mills C, Bhugra D, te Meerman S, Thoma S, Heinze M, et al. Psychiatrization of society: A conceptual framework and call for transdisciplinary research. Front Psychiatry . (2021) 12:645556. doi: 10.3389/fpsyt.2021.645556

58. Os Jv, Guloksuz S. Population salutogenesis—The future of psychiatry? JAMA Psychiatry . (2024) 81:115–5. doi: 10.1001/jamapsychiatry.2023.4582

59. Star SL, Griesemer JR. Institutional ecology, `Translations’ and boundary objects: amateurs and professionals in berkeley’s museum of vertebrate zoology, 1907-39. Soc Stud Sci . (1989) 19:387–420. doi: 10.1177/030631289019003001

60. Marsman A, Pries L-K, ten Have M, de Graaf R, van Dorsselaer S, Bak M, et al. Do current measures of polygenic risk for mental disorders contribute to population variance in mental health? Schizophr Bull . (2020). doi: 10.1093/schbul/sbaa086

61. van Os J. Personalized psychiatry: Geen vervanger van persoonlijke psychiatrie. Tijdschrift voor Psychiatr . (2018) 60:199–204.

62. Hyman SE. Psychiatric disorders: grounded in human biology but not natural kinds. Perspect Biol Med . (2021) 64:6–28. doi: 10.1353/pbm.2021.0002

63. Schleim S. Why mental disorders are brain disorders. And why they are not: ADHD and the challenges of heterogeneity and reification. Front Psychiatry . (2022) 13:943049. doi: 10.3389/fpsyt.2022.943049

64. Köhne ACJ. The relationalist turn in understanding mental disorders: from essentialism to embracing dynamic and complex relations. Philosophy Psychiatry Psychol . (2020) 27:119–40. doi: 10.1353/ppp.2020.0020

65. Terlouw G, Veer JTv’t, Prins JT, Kuipers DA, Pierie J-PEN. Design of a digital comic creator (It’s me) to facilitate social skills training for children with autism spectrum disorder: design research approach. JMIR Ment Health . (2020) 7:e17260. doi: 10.2196/17260

66. Terlouw G, Kuipers D, van ’t Veer J, Prins JT, Pierie JPEN. The development of an escape room–based serious game to trigger social interaction and communication between high-functioning children with autism and their peers: iterative design approach. JMIR Serious Games . (2021) 9:e19765. doi: 10.2196/19765

67. Kuipers DA, Terlouw G, Wartena BO, Prins JT, Pierie JPEN. Maximizing authentic learning and real-world problem-solving in health curricula through psychological fidelity in a game-like intervention: development, feasibility, and pilot studies. Med Sci Educator . (2018) 29:205–14. doi: 10.1007/s40670-018-00670-5

68. Kajamaa A. Boundary breaking in a hospital. Learn Organ . (2011) 18:361–77. doi: 10.1108/09696471111151710

69. Sajtos L, Kleinaltenkamp M, Harrison J. Boundary objects for institutional work across service ecosystems. J Service Manage . (2018) 29:615–40. doi: 10.1108/JOSM-01-2017-0011

70. Jensen S, Kushniruk A. Boundary objects in clinical simulation and design of eHealth. Health Inf J . (2014) 22:248–64. doi: 10.1177/1460458214551846

71. Terlouw G, Kuipers D, Veldmeijer L, van ’t Veer J, Prins J, Pierie J-P. Boundary objects as dialogical learning accelerators for social change in design for health: systematic review. JMIR Hum Factors . (2021). doi: 10.2196/31167

72. Wiener HJD. Conversation pieces: the role of products in facilitating conversation. Dukespace . (2017). Available online at: https://hdl.handle.net/10161/14430 .

73. Jaspers K. General psychopathology . Baltimore, Md: Johns Hopkins University Press (1998).

74. Parnas J, Sass LA, Zahavi D. Rediscovering psychopathology: the epistemology and phenomenology of the psychiatric object. Schizophr Bull . (2012) 39:270–7. doi: 10.1093/schbul/sbs153

75. Feyaerts J, Henriksen MG, Vanheule S, Myin-Germeys I, Sass LA. Delusions beyond beliefs: a critical overview of diagnostic, aetiological, and therapeutic schizophrenia research from a clinical-phenomenological perspective. Lancet Psychiatry . (2021) 8:237–49. doi: 10.1016/S2215-0366(20)30460-0

76. Ritunnano R, Kleinman J, Whyte Oshodi D, Michail M, Nelson B, Humpston CS, et al. Subjective experience and meaning of delusions in psychosis: a systematic review and qualitative evidence synthesis. Lancet Psychiatry . (2022) 9:458–76. doi: 10.1016/S2215-0366(22)00104-3

77. Köhne ACJ, van Os J. Precision psychiatry: promise for the future or rehash of a fossilised foundation? psychol Med . (2021) 51:1409–11. doi: 10.1017/S0033291721000271

78. Boevink WA. From being a disorder to dealing with life: an experiential exploration of the association between trauma and psychosis. Schizophr Bull . (2005) 32:17–9. doi: 10.1093/schbul/sbi068

79. Slade M, Longden E. Empirical evidence about recovery and mental health. BMC Psychiatry . (2015) 15. doi: 10.1186/s12888-015-0678-4

80. Leamy M, Bird V, Boutillier CL, Williams J, Slade M. Conceptual framework for personal recovery in mental health: systematic review and narrative synthesis. Br J Psychiatry . (2011) 199:445–52. doi: 10.1192/bjp.bp.110.083733

81. Groot PC, van Os J. How user knowledge of psychotropic drug withdrawal resulted in the development of person-specific tapering medication. Ther Adv Psychopharmacol . (2020) 10:204512532093245. doi: 10.1177/2045125320932452

82. Fusar-Poli P, Estradé A, Stanghellini G, Venables J, Onwumere J, Messas G, et al. The lived experience of psychosis: A bottom-up review co-written by experts by experience and academics. World Psychiatry . (2022) 21:168–88. doi: 10.1002/wps.20959

83. Jakobsson C, Genovesi E, Afolayan A, Bella-Awusah T, Omobowale O, Buyanga M, et al. Co-producing research on psychosis: a scoping review on barriers, facilitators and outcomes. Int J Ment Health Syst . (2023) 17. doi: 10.1186/s13033-023-00594-7

84. Orlowski S, Matthews B, Bidargaddi N, Jones G, Lawn S, Venning A, et al. Mental health technologies: designing with consumers. JMIR Hum Factors . (2016) 3. doi: 10.2196/humanfactors.4336

85. Vial S, Boudhraâ S, Dumont M. Human-centered design approaches in digital mental health interventions: exploratory mapping review. JMIR Ment Health . (2021) 9. doi: 10.2196/35591

86. Tindall RM, Ferris M, Townsend M, Boschert G, Moylan S. A first-hand experience of co-design in mental health service design: Opportunities, challenges, and lessons. Int J Ment Health Nurs . (2021) 30. doi: 10.1111/inm.12925

87. Schouten SE, Kip H, Dekkers T, Deenik J, Beerlage-de Jong N, Ludden GDS, et al. Best-practices for co-design processes involving people with severe mental illness for eMental health interventions: a qualitative multi-method approach. Design Health . (2022) 6:316–44. doi: 10.1080/24735132.2022.2145814

88. Veldmeijer L, Terlouw G, van Os J, van Dijk O, van ’t Veer J, Boonstra N. The involvement of service users and people with lived experience in mental health care innovation through design: systematic review. JMIR Ment Health . (2023) 10:e46590. doi: 10.2196/46590

89. Veldmeijer L, Terlouw G, van Os J, van ’t Veer J, Boonstra N. The frequency of design studies targeting people with psychotic symptoms and features in mental health care innovation: A research letter of a secondary data analysis. JMIR Ment Health . (2023) 11. doi: 10.2196/54202

90. Hawke LD, Sheikhan NY, Bastidas-Bilbao H, Rodak T. Experience-based co-design of mental health services and interventions: A scoping review. SSM Ment Health . (2024) 5:100309. doi: 10.1016/j.ssmmh.2024.100309

91. Scholz B, Gordon S, Happell B. Consumers in mental health service leadership: A systematic review. Int J Ment Health Nurs . (2016) 26:20–31. doi: 10.1111/inm.12266

92. Brotherdale R, Berry K, Branitsky A, Bucci S. Co-producing digital mental health interventions: A systematic review. Digital Health . (2024) 10. doi: 10.1177/20552076241239172

93. Scholz B. Mindfully reporting lived experience work. Lancet Psychiatry . (2024) 11:168–8. doi: 10.1016/S2215-0366(24)00007-5

94. Davis S, Pinfold V, Catchpole J, Lovelock C, Senthi B, Kenny A. Reporting lived experience work. Lancet Psychiatry . (2024) 11:8–9. doi: 10.1016/S2215-0366(23)00402-9

95. Palmer VJ, Bibb J, Lewis M, Densley K, Kritharidis R, Dettmann E, et al. A co-design living labs philosophy of practice for end-to-end research design to translation with people with lived-experience of mental ill-health and carer/family and kinship groups. Front Public Health . (2023) 11:1206620. doi: 10.3389/fpubh.2023.1206620

96. Tekin Ş. Participatory interactive objectivity in psychiatry. Philosophy Sci . (2022), 1–20. doi: 10.1017/psa.2022.47

97. Gardner C, Kleinman A. Medicine and the mind — The consequences of psychiatry’s identity crisis. New Engl J Med . (2019) 381:1697–9. doi: 10.1056/NEJMp1910603

98. Kendler KS. Potential lessons for DSM from contemporary philosophy of science. JAMA Psychiatry . (2022) 79:99. doi: 10.1001/jamapsychiatry.2021.3559

99. Fusar-Poli P, Estradé Andrés, Stanghellini G, Esposito CM, Rosfort René, Mancini M, et al. The lived experience of depression: a bottom-up review co-written by experts by experience and academics. World Psychiatry . (2023) 22:352–65. doi: 10.1002/wps.21111

100. Smits D-W, van Meeteren K, Klem M, Alsem M, Ketelaar M. Designing a tool to support patient and public involvement in research projects: the Involvement Matrix. Res Involvement Engagement . (2020) 6. doi: 10.1186/s40900-020-00188-4

101. Owens C, Farrand P, Darvill R, Emmens T, Hewis E, Aitken P. Involving service users in intervention design: a participatory approach to developing a text-messaging intervention to reduce repetition of self-harm. Health Expectations . (2010) 14:285–95. doi: 10.1111/hex.2011.14.issue-3

102. Nakarada-Kordic I, Hayes N, Reay SD, Corbet C, Chan A. Co-designing for mental health: creative methods to engage young people experiencing psychosis. Design Health . (2017) 1:229–44. doi: 10.1080/24735132.2017.1386954

103. Orlowski SK, Lawn S, Venning A, Winsall M, Jones GM, Wyld K, et al. Participatory research as one piece of the puzzle: A systematic review of consumer involvement in design of technology-based youth mental health and well-being interventions. JMIR Hum Factors . (2015) 2:e12. doi: 10.2196/humanfactors.4361

104. Gammon D, Strand M, Eng LS. Service users’ perspectives in the design of an online tool for assisted self-help in mental health: a case study of implications. Int J Ment Health Syst . (2014) 8. doi: 10.1186/1752-4458-8-2

105. Wendt T. Design for Dasein: understanding the design of experiences . San Bernardino, California: Thomas Wendt (2015).

106. Bateson G. Steps to an ecology of mind . Chicago: University Of Chicago Press (1972).

107. Schön DA. The Reflective Practitioner: How Professionals Think in Action . Aldershot, U.K: Ashgate, Cop (1991).

108. Verkerke GJ, van der Houwen EB, Broekhuis AA, Bursa J, Catapano G, McCullagh P, et al. Science versus design; comparable, contrastive or conducive? J Mechanical Behav Biomed Materials . (2013) 21:195–201. doi: 10.1016/j.jmbbm.2013.01.009

109. Zachar P. Psychopathology beyond psychiatric symptomatology. Philosophy Psychiatry Psychol . (2020) 27:141–3. doi: 10.1353/ppp.2020.0021

110. Foucault M. Madness and Civilization: A History of Insanity in the Age of Reason . London: Routledge (1961). doi: 10.4324/9780203278796

111. Star SL. This is not a boundary object: reflections on the origin of a concept. Science Technology Hum Values . (2010) 35:601–17. doi: 10.1177/0162243910377624

112. Jones N. Lived experience leadership in peer support research as the new normal. Psychiatr Serv . (2022) 73:125. doi: 10.1176/appi.ps.73201

113. Scholz B. We have to set the bar higher: towards consumer leadership, beyond engagement or involvement. Aust Health Rev . (2022) 46. doi: 10.1071/AH22022

114. Rivard L, Lehoux P, Hagemeister N. Articulating care and responsibility in design: A study on the reasoning processes guiding health innovators’ “care-making” practices. Design Stud . (2021) 72:100986. doi: 10.1016/j.destud.2020.100986

115. Dekker MR, Williams AD. The use of user-centered participatory design in serious games for anxiety and depression. Games Health J . (2017) 6:327–33. doi: 10.1089/g4h.2017.0058

116. Cross N. Designerly ways of knowing . London: Springer (2006).

117. Groeneveld B, Dekkers T, Boon B, D’Olivo P. Challenges for design researchers in healthcare. Design Health . (2018) 2:305–26. doi: 10.1080/24735132.2018.1541699

118. Scholz B, Stewart S, Pamoso A, Gordon S, Happell B, Utomo B. The importance of going beyond consumer or patient involvement to lived experience leadership. Int J Ment Health Nurs . (2023) 33:1–4. doi: 10.1111/inm.13282

Keywords: psychiatry, diagnosis, design, innovation, mental health care

Citation: Veldmeijer L, Terlouw G, van Os J, te Meerman S, van ‘t Veer J and Boonstra N (2024) From diagnosis to dialogue – reconsidering the DSM as a conversation piece in mental health care: a hypothesis and theory. Front. Psychiatry 15:1426475. doi: 10.3389/fpsyt.2024.1426475

Received: 01 May 2024; Accepted: 22 July 2024; Published: 06 August 2024.

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Copyright © 2024 Veldmeijer, Terlouw, van Os, te Meerman, van ‘t Veer and Boonstra. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Lars Veldmeijer, [email protected]

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