benefit of hypothesis statement

The ART of SAFe

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Monday, January 8, 2018

Effective feature templates for safe, introduction, how much detail is needed, and by when.

• Prior to WSJF assessment
• Prior to PI Planning

Feature Canvas

New Product: “The current state of the [domain] has focussed primarily on [customer segments, pain points, etc]. What existing products/services fail to address is [this gap] Our product/service will address this gap by [vision/strategy] Our initial focus will be [this segment]”

Existing Product: “Our [service/product] is intended to achieve [these goals]. We have observed that the [product/service] isn’t meeting [these goals] which is causing [this adverse effect] to our business. How might we improve [service/product] so that our customers are more successful based on [these measurable criteria]?”

“We believe this [business outcome] will be achieved if [these users] successfully achieve [this user outcome] with [this feature]”.

Sample Completed Canvas

A glimpse at how you might visualise your next WSJF estimation workshop

Detail beyond the Canvas

• User Journeys:  Some framing UX exploration is often very useful in preparing a Feature, and makes a great support to teams during PI planning.  
• Architectural Impact Assessment: Some form of deliberate architectural consideration of the potential impact of the feature is critical in most complex environments.  It should rarely be more than a page – I find a common approach is one to two paragraphs of text accompanied by a high level sequence diagram identifying expected interactions between architectural layers.
• Change Management Impacts: How do we get from deployed software to realised value?  Who will need training?  Are Work Instructions required?  

Tuning your Template

Who completes the canvas/template, 30 comments:.

Awesome work Mark! We have created some for clients too that we can't share. :-(

Thanks for sharing Mark - these are really useful. I really like the hypothesis statements for features and think that this is a major enhancement in SAFE 4.5. I wrote a blog post about it here: http://runningmann.co.za/2017/09/25/the-power-of-feature-hypotheses/ that you might be interested in.

These are awesome Mark. Thanks for sharing

Thanks for sharing your experience on this area with the community Mark. Feature Templates are a very common requirement for Agile practitioners, maybe you can persuade the SAFe community to include an artefact like this in the framework.

Information was good,i like your post. Looking forward for more on this topic. product management

This is great! Do you have the template format available so we don't have to replicate?

great stuff, how would you differentiate this from SAFe Epics

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Nice blog Mark How can I get a downloadable version of this Canvas?

I think you can make video about it. If you want to promote your channel on youtube you can buy youtube subscribers for it

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The Benefit Hypothesis: Creating Value-Driven Features

In Agile software development, delivering value to stakeholders is paramount. Agile Release Trains (ARTs) within the Scaled Agile Framework (SAFe) focus on continuously delivering solutions that meet customer needs and provide measurable benefits. One of the key elements in ensuring value delivery is the benefit hypothesis, which plays a crucial role in defining features. In this blog post, we’ll explore what a benefit hypothesis is, how it aids in defining features, and how it ensures that features deliver measurable benefits to stakeholders.

What is a Benefit Hypothesis?

A benefit hypothesis is a statement that describes the proposed measurable benefit a feature will provide to the end user or business. It is a critical component of each feature, alongside a short phrase giving the feature a name and context. The benefit hypothesis is essentially a prediction of the value that the feature will deliver once implemented.

The benefit hypothesis is not just a simple statement; it should be measurable and testable. It should clearly articulate the expected outcomes and benefits of the feature in a way that can be validated after implementation. By defining a benefit hypothesis, teams can focus on delivering features that not only meet user needs but also contribute to the overall business objectives.

Defining Features with Benefit Hypotheses:

When creating features, Product Managers collaborate with Product Owners and other key stakeholders to define the feature’s name, context, and benefit hypothesis. The benefit hypothesis helps guide the conversation and ensures that the feature is aligned with the desired business outcomes.

To create an effective benefit hypothesis, teams should consider the following:

1. Specific: The benefit hypothesis should be specific and clear, focusing on a single, measurable benefit.

2. Measurable: The benefit should be quantifiable, allowing teams to track and validate the feature’s impact.

3. Achievable: The benefit should be realistic and achievable within the given constraints and resources.

4. Relevant: The benefit should be aligned with the overall business objectives and stakeholder needs.

5. Time-bound: The benefit should have a specific timeframe for realization, helping teams prioritize and track progress.

Here’s an example of a well-defined feature with a benefit hypothesis:

Feature: Implement single sign-on functionality

Benefit Hypothesis: By implementing single sign-on, we expect to increase user registration by 15% and reduce user support tickets related to login issues by 20% within the first quarter after release.

In this example, the benefit hypothesis clearly states the expected measurable impact of the feature on user registration and support tickets, along with a specific timeframe for realization.

Using Benefit Hypotheses to Prioritize Features:

Benefit hypotheses not only help define features but also play a crucial role in prioritizing them. SAFe recommends using the Weighted Shortest Job First (WSJF) prioritization model to sequence jobs (features and capabilities) based on the economics of product development flow.

When calculating WSJF, the benefit hypothesis is a key factor in determining the “Cost of Delay” (CoD), which represents the economic impact of delaying the feature. Features with higher potential benefits will have a higher CoD, leading to a higher WSJF score and, consequently, a higher priority.

By prioritizing features based on their benefit hypotheses, teams can ensure that they are delivering the most value to stakeholders in the shortest amount of time, optimizing the economic impact of their development efforts.

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Validating Benefit Hypotheses:

A benefit hypothesis is not just a statement; it is a testable prediction of the value a feature will deliver. After implementing a feature, it is crucial to validate whether the benefit hypothesis holds true. This validation process helps teams learn and adapt their approach to delivering value.

To validate a benefit hypothesis, teams should:

1. Collect relevant data: Gather data related to the specific metrics outlined in the benefit hypothesis, such as user registration numbers or support ticket volumes.

2. Compare against baseline: Compare the collected data against the baseline metrics from before the feature implementation to determine the actual impact.

3. Analyze results: Analyze the results to determine whether the benefit hypothesis was accurate, and if not, investigate the reasons behind the discrepancy.

4. Learn and adapt: Use the insights gained from the validation process to inform future feature definition and prioritization, continuously improving the team’s ability to deliver value.

By validating benefit hypotheses, teams can ensure that they are delivering measurable value to stakeholders and continually optimizing their development efforts based on empirical evidence.

Benefit Hypotheses and Lean UX:

Benefit hypotheses align well with the Lean UX process model, which emphasizes rapid experimentation and validation. Lean UX incorporates the concept of the Minimum Marketable Feature (MMF), which represents the smallest set of functionality that delivers value to users and can be used to test the benefit hypothesis.

By combining benefit hypotheses with MMFs, teams can rapidly test their assumptions, gather feedback , and validate the value of their features before investing significant time and resources into full-scale development. This iterative approach helps teams mitigate risk, reduce waste, and ensure that they are consistently delivering value to stakeholders.

Conclusion:

The benefit hypothesis is a powerful tool for creating value-driven features in Agile Release Trains. By clearly articulating the expected measurable benefits of a feature, teams can ensure that they are focusing their efforts on delivering solutions that meet stakeholder needs and contribute to business objectives. Benefit hypotheses aid in prioritizing features based on their potential economic impact, helping teams optimize their development efforts.

Moreover, by validating benefit hypotheses after feature implementation, teams can continuously learn and adapt their approach to value delivery, ensuring that they are making data-driven decisions and continually improving their processes. When combined with Lean UX practices, such as Minimum Marketable Features, benefit hypotheses enable rapid experimentation and validation, further enhancing the team’s ability to deliver value quickly and efficiently.

By embracing the practice of defining and validating benefit hypotheses, Agile Release Trains can create a culture of value-driven development, consistently delivering solutions that meet stakeholder needs and drive business success.

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• Knowledge Base

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

Research bias

• 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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Home > Resources > Understanding Leading Indicators in Product Development and Innovation

Understanding Leading Indicators in Product Development and Innovation

It’s quite common for people to nod knowingly when you mention leading indicators, but in reality, few people understand them. I believe people struggle with leading indicators because they are counterintuitive, and because lagging indicators are so ingrained in our current ways of working. So, let’s explore leading indicators: what they are, why they’re important, how they’re different from what you use today, and how you can use them to improve your innovation and product development .

What Are Leading and Lagging Indicators?

Leading indicators (or leading metrics) are a way of measuring things today with a level of confidence that we’re heading in the right direction and that our destination is still desirable. They are in-process measures that we think will correlate to successful outcomes later. In essence, they help us predict the future.

In contrast, lagging indicators measure past performance. They look backwards and measure what has already happened.

Take the example of customer experience (CX). This is a lagging indicator for your business because the customer has to  have  the experience before you can measure it. While it’s great to understand how your customers perceive your service, by the time you discover it sucks it might be too late to do anything about it.

ROI is another example of a lagging indicator: you have to invest in a project ahead of time but cannot calculate its returns until it’s completed. In days gone by you might have worked on a new product and spent many millions, only to discover the market didn’t want it and your ROI was poor.

Online retailers looking for leading indicators of CX might look instead at page load time, successful customer journeys, or the number of transactions that failed and ended up with customer service. I often tell clients that if these leading indicators are positive, we have reason to believe that CX, when measured, will also be positive.

Don Reinertsen shares a common example of leading vs. lagging indicators: the size of an airport security line is a leading indicator for the lagging indicator of the time it takes to pass through security screening. This makes sense because if there is a large line ahead of you, the time it will take to get through security and out the other side will be longer. We can only measure the total cycle time once we’ve experienced it.

If you operate in a SAFe ®  context , the success of a new train PI planning (which is a lagging indicator) is predicated on leading indicators like identifying key roles, training people, getting leadership buy-in, refining your backlog, socializing it with the teams, etc.

Simple Examples of Successful Leading Indicators

The Tesla presales process is a perfect example of how to develop leading indicators for ROI. Tesla takes refundable deposits, or pre-orders, months if not years before delivering the car to their customers. Well before the cars have gone to production, the company has a demonstrated indicator of demand for its vehicles.

Back in the 90s, Zappos was experimenting with selling shoes online in the burgeoning world of e-commerce. They used a model of making a loss on every shoe sold (by not holding stock and buying retail) as a leading indicator that an online shoe selling business would be successful before investing in the necessary infrastructure you might expect to operate in this industry.

If you are truly innovating (versus using innovation as an excuse for justifying product development antipatterns, like ignoring the customer) then the use of leading indicators can be a key contributor to your innovation accounting processes. In his best-selling book,  The Lean Startup , Eric Ries explains this concept. If you can demonstrate that your idea is moving forward by using validated learning to prove problems exist, then customers will show interest before you even have a product to sell. Likewise, as Dantar P. Oosterwal demonstrated in his book,  The Lean Machine , a pattern of purchase orders can be a leading indicator of product development and market success.

Leading Indicators Can Be Near-term Lagging Indicators

Let’s loop back and consider the definitions of leading and lagging indicators.

• Lagging: Measures output of an activity. Likely to be easy to measure, as you’ve potentially already got measurement in place.
• Leading: Measures inputs to the activity. Often harder to measure as you likely do not do this today.

Think about the process of trying to lose weight. Weight loss is a lagging indicator, but calories consumed and exercise performed are leading indicators, or inputs to the desired outcome of losing weight.

While it’s true that both calories consumed and exercise performed are activities that cannot be measured until they’re completed, and therefore might be considered  near-term lagging indicators , they become leading indicators because we’re using them on the path to  long-term lagging indicators . Think back to the CX example: page load time, successful customer journeys, and failed transactions that end up with customer service can all be considered near-term lagging indicators. Yet we can use them as leading indicators on a pathway to a long-term lagging indicator, CX.

How to Ideate Your Leading Indicators

The most successful approach I’ve applied with clients over many years is based on some work by Mario Moreira, with whom I worked many moons ago. I’ve tweaked the language and application a little and recommend you create a Pathway of Leading to Lagging Indicators. To demonstrate this, I will return to the CX example.

If we walk the pathway, we can estimate that an acceptable page load time will lead to a successful user journey, which—if acceptable—will then lead to fewer failed transactions that revert to customer service, which ultimately will lead to a positive customer experience metric.

Work Backwards from Your Lagging Indicator

To create your Leading to Lagging Pathway, start from your lagging indicator and work backwards looking at key successful elements that need to be true to allow your lagging indicator to be successful.

At this stage, these are all presuppositions; as in, we believe these to be true. They stay this way until you’ve collected data and can validate your pathway. This is similar to how you need to validate personas when you first create them.

Add Feedback Loop Cycle Times

Once you have your pathway mapped out, walk the pathway forward from your first leading indicator and discuss how often you can and should record, analyze, and take action for that measure. You should make these feedback loops as short as possible because the longer the loop, the longer it will take you to learn.

All that’s left is to implement your Leading to Lagging Pathway. You may find a mix of measures, some which you measure today and some you don’t. For those you already do measure, you may not be measuring them often enough. You also need to put in place business processes to analyze and take action. Remember that if measures do not drive decisions, then your actions are a waste of resources.

Your leading indicator might be a simple MVP. Tools like QuickMVP can support the implementation of a Tesla-style landing page to take pre-orders from your customers.

Applying Leading Indicators in Agile Product Management

A common anti-pattern I see in many product management functions is a solution looking for a problem. These are the sorts of pet projects that consume huge amounts of R&D budget and barely move the needle on profitability. Using design thinking and Lean Startup techniques can help you to validate the underlying problem, determine the best solution, and identify whether it’s desired by your potential customers and is something you can deliver profitably.

In SAFe, leading indicators are an important element of your epic benefit hypothesis statement. Leading indicators can give you a preview of the likelihood that your epic hypothesis will be proven, and they can help deliver this insight much earlier than if you weren’t using them. Insight allows you to pivot at an earlier stage, saving considerable time and money. By diverting spending to where it will give a better return, you are living by SAFe principle number one,  Take an economic view .

Let’s look at some working examples demonstrating the use of leading indicators.

I hope you can now see that leading indicators are very powerful and versatile, although not always obvious when you start using them. Start with your ideation by creating a Leading to Lagging Pathway, working back from your desired lagging indicator. If you get stuck, recall that near-term lagging indicators can be used as leading indicators on your pathway too. Finally, don’t feel you need to do this alone, pair or get a group of people together to walk through this process, the discussions will likely be valuable in creating alignment in addition to the output.

Let me know how you get on. Find me on the  SAFe Community Platform  and  LinkedIn .

About Glenn Smith

Glenn Smith is SAFe Program Consultant Trainer (SPCT), SPC, and RTE working for Radtac as a consultant and trainer out of the UK. He is a techie at heart, now with a people and process focus supporting organizations globally to improve how they operate in a Lean-Agile way. You will find him regularly talking at conferences and writing about his experiences to share his knowledge.

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• Benefit Hypothesis

A concept that suggests that people are more likely to accept a change if they can see the potential benefits it will bring. Product Glossary Benefit Hypothesis Also called: Benefit Theory, Positive Outcome Theory, Positive Consequence Theory, Positive Reinforcement Theory, Reward Theory, Reinforcement Theory, and Reinforcement Hypothesis See also: Forced Analogy , Hypothesis Statement , Objectives and Key Results , Powers of Ten , Problem Statement , Benefit Hypothesis Relevant metrics: Conversion Rate, Retention Rate, User Engagement, Cost per Acquisition, and Customer Lifetime Value In this article What is Benefit Hypothesis

Benefit Hypothesis is a concept used to describe the idea that a product should be designed to provide a benefit to the user.

It is based on the assumption that users will be more likely to use a product if it provides them with a benefit. The benefit can be tangible, such as a monetary reward, or intangible, such as increased convenience or improved user experience.

The benefit hypothesis is used to guide product design decisions, as it helps to ensure that the product is designed to provide a benefit to the user. It is also used to evaluate the success of a product, as it helps to determine whether the product is providing the desired benefit to the user.

Where did Benefit Hypothesis come from?

The term “Benefit Hypothesis” was first coined by evolutionary biologist Robert Trivers in 1971. Trivers proposed that the evolution of altruism, or selfless behavior, could be explained by the idea that individuals could benefit from helping others. He argued that if an individual helps another, the other individual may be more likely to help them in the future, thus creating a mutually beneficial relationship. This idea has since been expanded upon and is now used to explain a variety of social behaviors, such as cooperation, reciprocity, and even the evolution of language.

Exploring the Benefit Hypothesis

The Benefit Hypothesis is a concept that suggests that social interaction can have a positive impact on individuals. This hypothesis is based on the idea that people are naturally social creatures and that social interaction can be beneficial to their mental and physical health. It is believed that social interaction can help to reduce stress, improve mood, and even increase life expectancy.

The Benefit Hypothesis has been studied extensively in the fields of psychology and sociology. Studies have shown that people who engage in regular social interaction are more likely to have better mental health, better physical health, and even longer life expectancies. This is because social interaction can provide a sense of belonging, support, and companionship, which can help to reduce stress and improve mood. Additionally, social interaction can provide a sense of purpose and meaning, which can help to increase life satisfaction.

The Benefit Hypothesis in Product Development

Identifying what customer needs and expectations are can often be a challenge, especially in a crowded and rapidly changing market. The Benefit Hypothesis is a valuable tool for product developers looking to create products that truly deliver value to their customers.

The Benefit Hypothesis is a hypothesis that states the benefits that a product provides to its customers is the primary reason for its success. In other words, the more benefits a product provides, the more successful it will be. This hypothesis is based on the idea that customers will choose products that offer the most value to them, and that this value is determined by the benefits they receive from using the product.

Applying Benefit Hypotheseses

Using the Benefit Hypothesis in product development is a straightforward process that involves four key steps:

• Identifying the target customer and their needs - This step involves understanding who the product is being developed for and what their needs and expectations are.
• Determining the potential benefits of the product - This step involves considering the potential benefits that the product could provide to its target customers, such as improved efficiency, convenience, or savings.
• Creating a hypothesis based on potential benefits - This step involves creating a hypothesis that states the benefits that the product will provide to its target customers and how these benefits will be delivered.
• Validating the hypothesis through testing and research - This step involves testing the hypothesis through user research, customer feedback, and other methods to ensure that the product truly delivers the benefits that were promised.

Benefits of using Benefit Hypotheses

• Allows for a more comprehensive understanding of how people make decisions . The Benefit Hypothesis provides a framework for understanding how people make decisions by considering both the costs and benefits of a given action. This helps to explain why people may choose to do something even if it is not in their best interest.
• Helps to identify potential areas of improvement . By understanding the costs and benefits of a given action, the Benefit Hypothesis can help to identify areas where improvements can be made. This can help to inform decision-making and ensure that the best possible outcome is achieved.
• Provides a more accurate picture of decision-making . By considering both the costs and benefits of a given action, the Benefit Hypothesis provides a more accurate picture of how people make decisions. This can help to ensure that decisions are made in a more informed and rational manner.

Challenges of applying Benefit Hypotheses

• Establishing a clear definition of the term . The Benefit Hypothesis is a complex concept that can be difficult to define in a concise and accurate manner. It is important to ensure that all stakeholders understand the concept and its implications.
• Determining the appropriate level of benefit . It can be difficult to determine the appropriate level of benefit that should be provided to stakeholders. This requires careful consideration of the potential costs and benefits associated with the implementation of the Benefit Hypothesis.

The Apple iPhone was developed based on the hypothesis that customers would value a device that combined the functions of a mobile phone, music player, and internet browser in one device. The iPhone was a huge success because it delivered on this promise, providing customers with a device that offered unprecedented convenience and functionality.

Starbucks has applied the Benefit Hypothesis to its business model by offering customers a wide variety of coffee and tea products at competitive prices. This has allowed them to build a loyal customer base and increase their market share. By offering customers a wide selection of products and services, Starbucks has been able to create a competitive advantage over other coffee and tea retailers.

• What is the purpose of the benefit hypothesis? Hint The purpose of the benefit hypothesis is to identify and analyze the potential benefits of a proposed policy or program.
• What are the potential benefits of applying the benefit hypothesis? Hint Potential benefits of applying the benefit hypothesis include improved decision-making, increased efficiency, and cost savings.
• What are the potential risks of applying the benefit hypothesis? Hint Potential risks of applying the benefit hypothesis include unintended consequences, inaccurate data, and inadequate evaluation.
• What are the potential implications of applying the benefit hypothesis? Hint Potential implications of applying the benefit hypothesis include changes in public opinion, changes in public policy, and changes in public behavior.
• How will the benefit hypothesis be evaluated? Hint The benefit hypothesis will be evaluated by assessing the potential benefits and risks associated with the proposed policy or program.
• What data or evidence is available to support the benefit hypothesis? Hint Data and evidence to support the benefit hypothesis can include economic analysis, cost-benefit analysis, and other forms of research.
• How will the benefit hypothesis be implemented? Hint The benefit hypothesis will be implemented by developing a plan to identify and analyze the potential benefits and risks associated with the proposed policy or program.
• What are the potential unintended consequences of applying the benefit hypothesis? Hint Potential unintended consequences of applying the benefit hypothesis include unintended economic, social, and environmental impacts.
• How will the benefit hypothesis be monitored and evaluated? Hint The benefit hypothesis will be monitored and evaluated by assessing the outcomes of the proposed policy or program.
• What are the potential longterm effects of applying the benefit hypothesis? Hint Potential long-term effects of applying the benefit hypothesis include changes in public opinion, changes in public policy, and changes in public behavior.

You might also be interested in reading up on:

• Forced Analogy
• Hypothesis Statement
• Objectives and Key Results
• Powers of Ten
• Problem Statement
• Influence : The Psychology of Persuasion by Robert Cialdini (1984)
• Predictably Irrational : The Hidden Forces That Shape Our Decisions by Dan Ariely (2008)
• Nudge : Improving Decisions About Health, Wealth, and Happiness by Richard Thaler (2008)
• Thinking, Fast and Slow by Daniel Kahneman (2011)
• Freakonomics : A Rogue Economist Explores the Hidden Side of Everything by Steven D. Levitt and Stephen J. Dubner (2005)

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Minimum Requirements for a Feature

Minimum requirements for a feature: what is a benefit hypothesis, minimum requirements for a feature: who writes acceptance criteria, feature definition.

A feature, within the scaled Agile definition (SAfe), requires a benefits hypothesis and acceptance criteria. These establish what and why you are testing and how you will determine success or failure. Each feature will usually have three key components that form the minimum requirements: Beneficiaries. These are needed upfront to establish the hypothesis and the acceptance criteria. Benefit hypothesis.  Acceptance criteria.

.css-uphcpb{position:absolute;left:0;top:-87px;} How do you write a feature in agile?

The minimum requirements already discussed can be made more detailed by covering a few related areas:

The beneficiaries.

The benefit hypothesis. 

The feature’s business value.

A clear feature description.

Acceptance criteria.

The two fundamental elements of the benefits hypothesis and the acceptance criteria can be unpacked in a little more detail to illustrate their individual and collective roles for features.

The benefit hypothesis is the business value that the feature is expected to deliver. Similar to a scientific hypothesis, this is a statement that will ultimately be tested to see if it is correct. A good formula to use is:

If (proposition), then (benefit)

The proposition is what your team plans to deliver, while the benefit is the value that this will deliver. Benefits can be business-side and include:

Increased efficiency.

Greater transparency.

Cost reductions.

Improved data streams.

Increased revenue.

On the client-side, benefits can include:

Increase customer satisfaction.

Improved functionality.

Greater simplicity for better customer experiences (CX).

How likely is this proposition able to deliver this benefit? 

Is this feature’s success rate quantifiable? 

You must be able to validate your hypothesis to measure the relative success or lack of success of the related feature. Ongoing optimization or even a decision to pivot will not be possible without the ability to quantify how well the proposition succeeded in delivering the benefit.

In Scaled Agile Frameworks (SAFe) a feature’s acceptance criteria are usually written by the stakeholder or the product owner. The acceptance criteria should provide a framework to measure whether the benefit is being delivered by the proposition. In other words, has the feature shown the benefit hypothesis to be correct? If not, is it possible to optimize or would it be better to pivot?

The main functions of acceptance criteria are:

Determine if the feature has been implemented correctly.

Establish whether the business benefits are being delivered.

Mitigate implementation risks.

Facilitate early validation testing to prevent unnecessary costs and effort.

Inform user stories and functional tests.

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The SAFe® Epic – an example

We often have questions about what a “good” sufficiently developed SAFe Epic looks like. In this example we use with clients during the Lean Portfolio Management learning journey, we dive into an example of real-world details behind an epic hypothesis statement.

For now, we have not provided a fully developed SAFe Lean Business Case as an example because business cases are typically highly contextual to the business or mission. That being said please remember that the core of the business case is established and driven from the Epic Hypothesis Statement and carried over into the documented business case.

Agile Lifecycle Management Solution Enabler Epic

Epic Owner: John Q. Smith

Epic Description

The big awesome product company requires a common platform for collaboration on work of all types across the portfolio for all teams, managers, architects, directors, and executives including customer collaboration and feedback loops. This solution will solve the problems in our system where we have poor quality or non-existent measurement and multiple disparate systems to manage and report on work.

For the Portfolio Teams

Who needs to manage their work, flow, and feedback

The single-source system of record for all work

IS A web-based software tool suite that provides customizable workflows that support the enterprise strategic themes related to creating better business and IT outcomes using guidance from the Scaled Agile Framework for Lean Enterprises (SAFe), Technology Business Management (TBM), and Value Stream Management (VSM) via the Flow Framework

THAT will provide a common place for all customers and portfolio stakeholders to have a transparent vision into all of the work occurring in the system/portfolio, provide a mechanism to manage capacity at scale, and enable easier concurrent road mapping  

UNLIKE the current array of disparate, ad hoc tools and platforms

OUR SOLUTION will organize all work in a holistic, transparent, visible manner using a common enterprise backlog model combined with an additive enterprise agile scaling framework as guidance including DevOps, Lean+Systems Thinking, and Agile

Business Outcomes:

• Validate that the solution provides easy access to data and/or analytics, and charts for the six flow metrics: distribution, time, velocity, load, efficiency, and predictability for product/solution features (our work). (binary)
• The solution also provides flow metrics for Lean-Agile Teams stories and backlog items. (binary)
• 90% of teams are using the solution to manage 100% of their work and effort within the first year post implementation
• All features and their lead, cycle, and process times (for the continuous delivery pipeline) are transparent. Feature lead and cycle times for all value streams using the system are visible. (binary)
• Lean flow measurements — Lead and cycle times, six SAFe flow metrics, and DevOps metrics enabled in the continuous delivery pipeline integrated across the entire solution platform (binary)
• Activity ratios for workflow, processes, and steps are automatically calculated (binary)
• Percent complete and accurate (%C&A) measures for value streams automatically calculated or easily accessible data (binary)
• Number of documented improvements implemented in the system by teams using data/information sourced from the ALM solution > 25 in the first six months post implementation
• Number of documented improvements implemented in the system by teams using data/information sourced from the ALM solution > 100 in the first year post implementation
• Flow time metrics improve from baseline by 10% in the first year post implementation (lead time for features)
• Portfolio, Solution/Capability, and Program Roadmaps can be generated by Lean Portfolio Management (LPM), Solution Management, and Product Management at will from real-time data in the ALM (binary)
• Roadmaps will be available online for general stakeholder consumption (transparency)
• Increase customer NPS for forecasting and communication of solution progress and transparency of execution by 20% in the first year post implementation (survey + baseline)
• Build a taxonomy for all work including a service catalog (binary)
• Run the system and the system produces the data to produce the capacity metrics for all value streams to enable the LPM guardrail (binary)
• Stops obfuscation of work hidden in the noise of the one sized fits all backlog model (everything is a CRQ/Ticket) and allows for more accurate and representative prioritization including the application of an economic decision-making framework using a taxonomy for work (binary)
• Enables full truth in reporting and transparency of actual flow to everyone, real-time – including customers (100% of work is recorded in the system of record)
• Enables live telemetry of progress towards objectives sourced through all backlogs, roadmaps, and flow data and information (dependent)
• 90% of teams are using the solution to manage 100% of their capacity within the first year post implementation

Leading Indicators:

• Total value stream team member utilization > 95% daily vs. weekly vs. per PI
• Low daily utilization < 75% indicates there is a problem with the solution, training, or something else to explore
• % of teams using the ALM solution to manage 100% of their work and effort
• Number of changes in the [old solutions] data from the implementation start date
• Usage metrics for the [old solutions]
• We can see kanban systems and working agreements for workflow state entry and exit criteria in use in the system records
• Teams have a velocity metric that they use solely for the use of planning an iterations available capacity and not for measuring team performance (only useful for planning efficiency performance)
• Teams use velocity and flow metrics to make improvements to their system and flow (# of improvements acted from solution usage)
• Teams are able to measure the flow of items per cycle (sprint/iteration) and per effort/value (story points; additive)
• Program(s)[ARTs] are able to measure the flow of features per cycle (PI) and per effort/value (story points; additive from child elements)
• Portfolio(s) are able to measure the flow of epics per cycle (PI) and per effort/value (story points; additive from child elements)
• % of total work activity and effort in the portfolio visible in the solution
• Show the six flow metrics
• Features (Program) – current PI and two PI’s into the future
• Epics and Capabilities – current PI up to two+ years into the future
• are the things we said we were going to work on and what we actually worked on in relation to objectives and priorities (not just raw outputs of flow) the same?
• The portfolio has a reasonable and rationalized, quality understanding of how much capacity exists across current and future cycles (PI) in alignment with the roadmap
• Identification and reporting of capacity across Portfolio is accurate and predictable;
• Identification of Operational/Maintenance-to-Enhancement work ratio and work activity ratios and % complete and accurate (%C&A) data readily available in the system
• including operations and maintenance (O&M) work and enhancements,
• highlighting categories/types of work
• Work activity ratios are in alignment with process strategy and forecasts, process intent, and incentivizing business outcomes;
• allows leadership to address systemic issues;
• data is not just reported, but means something and is acted upon through decision-making and/or improvements
• # of epics created over time
• # of epics accepted over time
• # of MVP’s tested and successful
• Parameters configured in the tool to highlight and constrain anti-patterns
• Stimulates feedback loop to assist in making decisions on whether to refine/improve/refactor and in that case, what to refine/improve/refactor
• Strategic themes, objectives, key results, and the work in the portfolio – Epics, Capabilities, Features, Stories traceability conveyed from Enterprise to ART/Team level

Non-Functional Requirements

• On-Premise components of the ALM solution shall support 2-Factor Authentication
• SaaS components of the ALM solution shall support 2-Factor Authentication and SAML 2.0
• The system must be 508 compliant
• The system must be scalable to support up to 2000 users simultaneously with no performance degradation or reliability issues
• Must be the single-source system for all work performed in the portfolio and value streams.
• ALM is the single-source system of record for viewing and reporting roadmap status/progress toward objectives

Building your Experiment – The Minimum Viable Product (MVP)

Once you have constructed a quality hypothesis statement the product management team should begin work on building the lean business case and MVP concept. How are you going to test the hypothesis? How can we test the hypothesis adequately while also economically wrt to time, cost, and quality? What are the key features that will demonstrably support the hypothesis?

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Humorous Anecdotes, Observations & Accounts of Marathon Running & Agile Adventures.

The Power of Feature Hypotheses

One of the improvements SAFe version 4.5 introduced was incorporating practices from “ The Lean Startup ” into the framework – specifically the use of benefit hypothesis statements into features and epics. This is a story of how well this worked for us.

We were worried about the standard of feature writing across our teams and also wanted to bring them up to speed with the SAFe 4.5 feature hypothesis thinking. Therefore, we organised a Friday afternoon “Lunch and Learn” session for interested team members.

The main objective was to go over a practical example from one of our feature teams and convert an existing feature into a ‘ valuable feature hypothesis statement ‘.

[If you are unfamiliar with the Lean Startup concept of hypotheses, please see supporting post:  Using Hypothesis Statements for Features in Software Development ]

Getting A Feature Benefit Hypothesis Statement

We started with the feature captured below that, no disrespect to team, was not very well written. The definition I like to use for a well written feature is that: ‘ someone outside the team can read it once and understand what needs to be done ’. At this stage I don’t even think that most of the team understood the feature.

Luckily we had the product owner (PO) in the room. We asked him a few probing questions that went something like this.

Team: Why are we doing this change? What is the benefit? PO: We need to update the audit report fields for our customers. Team: Why do they need the additional fields. PO: So that customers can check for errors before they submit them to us for processing. Team: Why do they check for errors? How does this work? PO: All customers have a QA person or someone similar checking these reports to prevent errors being processed. When errors get missed and processed, it wastes time and causes a lot of frustration. Team: So what exactly is the current problem? PO: Today, customer auditors can’t see all the relevant information on the audit reports for cross-checking and referencing so many errors are still processed. Team: What is the intended result of this change? PO: We’ll reduce the error rate by 95%. [And BAM! We get our hypothesis]

Interesting takeout: The PO did not come right out with the “95% error reduction” even though it was in his head from the start – it required a conversation to get his knowledge shared with the rest of the team. This is part of the magic of conversation within collocated teams – and the importance of having a PO who works with the team.

The benefit hypothesis makes the “why” clear to without having to write a long document. Everyone in the room quickly came to the same understanding as to what needed to be done and why. A well worded hypothesis statement helps remove ambiguities and focuses the team on what really needs to be done. [To understand why “why?” is important – see Simon Sinek’s TED Talk below.]

The other (perhaps even more) positive outcome is that it gives “purpose” to the teams’ work. I asked the group, “Would you rather (a) update some fields on an audit report or (b) reduce error rates by 95%?” – unsurprisingly there was unanimous agreement that (b) was the more exciting option.

“Reducing error rates by 95%” gives meaning to the team’s work. I am unlikely to go home and proudly tell my kids I updated some fields on an audit report. But telling them that I helped reduce foreign exchange error rates by 95% makes my job as a developer on a transactional banking system sound sexy and important!

Feature Acceptance Criteria & Slicing

We then moved onto the feature acceptance criteria (AC). The simple way I view feature AC is, “ How will we UAT the feature to know that it’s complete and fit for purpose? ” (as opposed to user story AC which are the actual unit test cases).

If you are not able to write clear AC you don’t know enough to proceed with the feature so this was a good test of the strength of our feature hypothesis. Once again it was an interesting and valuable discussion with participants throwing various questions at the PO. A summary is below:

Which countries are in scope?

The PO initially said “all countries”. Further conversation sliced it down to two countries (South Africa and Uganda) where there was definite current need – from there it made sense to split the feature into one for each country. South Africa had the most urgent need so we focused on that and the lower priority Uganda feature went onto the backlog (where it will remain until it becomes priority). The requirement that the initial feature for South Africa be scalable for other countries was included as a non-functional requirement.

Attempted slipping in of a production defect

The PO tried to slip a production defect into the AC (the format of the onscreen and printed reports are different). The team deftly managed to slice the defect off the feature (the defect fix is roughly the same size as the eventual feature). The valid defect was added to the team backlog (where the PO can prioritise it against other work to determine when it will get fixed).

Attempted scope creep

The PO also tried to slip a new requirement into the AC – the ability to be able to save audit reports as a .pdf file. Once again the team deftly convinced the PO that this was not part of the minimum viable product (MVP) by referring to the hypothesis statement (i.e. we don’t need to save to .pdf to test the hypothesis). The valid requirement was also added to the team backlog (and the PO gets to decide when it’s priority enough to be built).

Interesting takeout: In my opinion, the PO was doing his job perfectly – trying to get as much as possible into the feature to maximise the customer benefit. Because he’s part of the overall team having the conversation he gets to understand the trade-offs involved with different decisions. When posed with the question, “Do you want to have the feature done in 2 weeks if we just do MVP or do you want to wait for 2 months if we do ‘ all this other stuff ‘?” a good PO will always go for “ small and fast “.

By reducing the feature to a single (highest priority) country and omitting other requirements that (whilst important) had no impact on the hypothesis statement, the feature ended up being at least 10 times smaller than if we’d tried to include everything. Slicing the feature down to get the most value with the least amount of work drastically increases the speed with which it can be built and dramatically reduces risk.

The team left the room knowing exactly what needed to be done. More importantly everyone had agreed on what didn’t need to be done “right now” (i.e. in the next sprint) because they were not MVP (however these requirements have been added to the team backlog and can be done when the PO prioritises them). The PO was also part of all the decisions taken so he should get no nasty surprises when the feature is presented back as “done”.

Conclusion: Super-Quick Delivery

The best part of this story was that two weeks later when I asked the team “How’s the feature going?”, the reply was “It’s already done.”

The team was able to fully design, build and test a valuable feature in less than two weeks (fitting easily into a 2-week sprint). In the past a feature like this would usually take 12 months to deliver (see the supporting post “ 6 Reasons: From 12 Months to 2 Weeks for Feature Delivery “). By slicing the feature down to its true MVP and the team knowing exactly what was needed and why, the feature flew through the development value stream.

Of course, now the real test is to measure whether our hypothesis proves true and we successfully reduce the error rate by 95% – let’s hope so! We’ll know pretty soon…

The actual reduction in client error rates was 80%. As it stands this has been deemed sufficient. There are no plans to build additional features to further reduce the error rates (as there have been higher priority features to work on). Likewise, the production defect has not been fixed (and may never be) since it has never been a priority compared to other more beneficial work.

I delivered a presentation on “The Power of Feature Hypotheses” at Agile Africa 2018 using this and other examples. Here is the video link from the conference:  https://youtu.be/CUVX1AiqQak?list=PLp6xQ3fl72zIu8FJjDtBUFUS0w1FPQhPZ

I am also more than happy to submit a speaker proposal for your conference. Feel free to contact me via email: [email protected] or Twitter @runningmann100 /  @StuartDMann .

4 Replies to “The Power of Feature Hypotheses”

• Pingback: 6 Reasons: From 12 Months to 2 Weeks for Feature Delivery - The Running Mann
• Pingback: Using Hypothesis Statements for Features in Software Development - The Running Mann

Good account of what we discussed in the session and what was learnt. Going forward it will definitely provide a mechanism for us to access information on the sessions to further unpack.

• Pingback: How George Costanza, Frogger & a Craving For Sushi Help Explain Features & User Stories - The Running Mann

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How To Write A Hypothesis Guide And Detailed Instructions

Whether you’re studying for a college degree, MBA, or Ph.D., developing a hypothesis for your research is mandatory. You must know how to write a good hypothesis to impress your professors. Now, how should a hypothesis be written?

This is where some students get confused and exhausted. You already know that you’re to formulate a hypothesis around something testable. But you don’t know how to create hypotheses based on previous observations that you would later explain in your paper or journal.

In this article, you’ll learn what a hypothesis is, how to make a hypothesis, examples of how to write hypothesis statement, and how to go about yours.

What Is A Hypothesis?

A hypothesis is a statement that is not proven, and it’s an assumption that you’ll base your research on. They must be testable: they must have answers that can be checked with experiments and evidence.

The theory around your hypothesis becomes valid when it’s proven to be true through experiments. Scientists have rules for writing that make their chemistry, physics, and biology research reproducible.

An essential part is that they must understand the experiments of others so that they can build on them and improve them. These rules define how scientists write about science. This rule applies to hypotheses, too.

Why Do You Need A Hypothesis?

Writing a good hypothesis is a key part of any scientific exploration. It allows a broad and open-ended question that compels you to investigate. There are many other reasons, including:

It’s different from a theory because a theory is something like:

“The earth orbits around the sun.”

This is not testable because we know that it’s true. A theory is more like an explanation for why something happens, while a hypothesis is a guess about what will happen and why it would.

A hypothesis is a statement of the relationship you’ve observed in a pair of variables. The easiest way to think about it is that the hypothesis is your testable statement for your research project.

You would typically use your background knowledge and experience as a researcher to come up with this statement before you set out to collect data. A good hypothesis will give you insight into what kind of data you need to collect to answer the question (or provide evidence).

For example:

“People who live in cities have higher stress levels than those who live in rural areas because there are more people around them all day long!”

This hypothesis would then lead us to ask questions like “How do we measure stress?” or “What factors contribute to stress?” You’ll provide answers to these questions with the paper.

A hypothesis can be proven or disproven throughout an experiment. The most common way to disprove a hypothesis is through statistical significance testing. This entails using probability and data analysis to show that there’s no practical difference between the two compared groups.

The hypothesis is a testable statement about how the world works. It’s also a way to properly arrange and structure your data. Without a hypothesis, you won’t even know what to set your scientific experiment on. A hypothesis is what you’ll use to predict what will happen in the future, and the data you collect during the research will help validate or disprove this.

In science, you’re always trying to figure out why things happen the way they do and what factors affect them. When you know how something works, “why do some people get sick while others don’t?” You might make up a hypothesis to test your idea: “People who are exposed to germs get flu symptoms.” Here’s how to start a hypothesis as the answer lets you determine whether your idea is right or wrong; an experiment then validates (or disproves) it.

Now that you know why you need to formulate a testable hypothesis, learn how to write a research hypothesis with tangible examples.

How To Write A Hypothesis

Before you start your experiments in the lab, it’s important to take some time to think about what you’re trying to achieve. After all, you can’t know your research destination until you plan it beforehand. This is why mastering how to state a hypothesis gives room for healthy predictions. Here’s how you formulate hypothesis:

Your first step is to determine what you want to investigate. You can start with a question you’d like to answer or a problem that needs solving.For example, if you’re a teacher trying to improve your students’ reading skills, you might ask:

“What techniques can I use for my students to boost reading comprehension scores on their standardized tests?”

This could also be stated as “Do test-taking strategies lead to improved standardized test scores?”

Once your question pops in your mind, especially while reflecting on a scientific paper you’ve read or a documentary you saw, write it down and commence research.

You need some facts to state a hypothesis and prove it. It might be tricky to get these facts, and you’ll want to look for relevant and irrelevant information.

Relevant information is directly related to your hypothesis. For example, your relevant sources would be academic, examination, and psychology journals, quantitative data or news outlets for the above statement.

Irrelevant information is any other kind of data, and this could be random news outlets or interviews that could help bolster what your assumptions are.

Use the word “because” to indicate that your variable causes or explains another variable. For example: If we are testing whether exercise leads to weight loss, our sentence might look like this:

“Consistent gym practice causes weight loss because it burns calories and gets the body in shape.”

You need to identify if your hypothesis is testable or if it’s an opinion you can’t prove. You can’t test what you don’t know or can’t prove. So you’d need to rewrite your hypothesis if you think it’s not testable.

Your hypothesis should be clear, concise, testable, specific, and relevant. The best way to do this is to write a brief summary of your hypothesis in the form: “If X happens, then Y will happen.”

Here’s a sample hypothesis:

“If I add 15 minutes to my sitting time everyday, then my body mass index (BMI) will reduce by 5 points in three months.”

Now that you’ve defined your idea, it’s time for the actual experiment to determine whether it’ll work.

How To Write A Hypothesis Statement: Example Of A Hypothesis

There are numerous examples of a hypothesis statement you can take a clue from. A scientific hypothesis examines two variables that need evidence-based research to be considered valid. For example:

“If I increase the amount of water applied to a plant garden, then it will make it grow faster.”

You have identified the independent and dependent variables in this statement. The independent variable is “amount of water applied,” and the dependent variable is “grow faster.” You also included a control group, which is important in scientific experiments to eliminate bias from other factors that could influence your results.

In this case, you are comparing how much growth there would be if you increase the amount of water versus how much growth there would be if you do not increase it.

You then need to research the topic in detail and design an experiment before you can write your report. The first step is to decide what you’re going to measure, how you’ll measure it, and how many times you’ll do this so that it’s accurate.

Once you’ve measured your experiment, interpreting the results can be challenging. You should look at graphs or charts of your data to see if any patterns or trends might indicate a cause-and-effect relationship between two things (like applying more water to the plant garden and faster growth).

After looking at the results of your experiment and deciding whether or not they support your original hypothesis, use this new knowledge in your conclusion. Write up something like:

“Based on my findings, it’s clear that applying more water to any plant garden would make the plant garden grow faster and greener.”

Then, write an introduction section where you can explain why this project interests/matters/is relevant to your reader. At this point, your hypothesis is no longer an educated guess. It started as one (with the observation or thoughts/idea) and ended as verifiable.

Format For Hypothesis: How Should A Hypothesis Be Written?

The usual format of a hypothesis is If – (then) – because.

Because we have the idea that if a hypothesis is formatted as an if-then statement, it’s clear what the hypothesis is about. This can be helpful for your readers and yourself if you ever need to come back and look at your work.

So, now that you know how to format it correctly (and why) let’s look at some hypothesis examples.

“If snow falls, then I’ll catch a cold when I get outside because cold can be a result of heavy snow.”

“If anyone in my family eats cake, then we will feel sick because the cake contains ingredients we are allergic to.”

“Some grasses never grow because they’re stumped every day.”

All these show that two variables must come together in the sentence. The variables must also be a probability the research attempts to solve to make them valid statements.

How To Know Your Hypothesis Is Good

Now that you know how to create a hypothesis, you need to know if it’s good through these pointers:

State a Hypothesis as Clearly as Possible You can choose precise words that are neither ambiguous nor too technical. You should also avoid jargon and words with multiple meanings to keep your language simple and clear. Don’t use fancy or pretentious words unless they’re absolutely necessary for the meaning you want to convey, and make sure you’ve used them in their correct context. In addition, use a tone of voice appropriate to the audience. A scientific paper may need more formal language than an article for popular consumption. A Good Hypothesis Should Explain the Bond Between Multiple Variables The main purpose of forming a hypothesis is to explain the relationship between multiple variables clearly. The relationship should be testable for it to be proven. This is, why if X leads to Y, what is in between that connects X and Y? This must reflect in the hypothesis as it’s the factor that’ll be experimented. A Hypothesis should Be Testable This means that your hypothesis should be a statement that can be proven or disproven with an experiment. You want to make sure your hypothesis is specific enough to guide you towards the right experiment but not so specific that it eliminates any other possible outcomes of your experiment. Also, a hypothesis should not make claims about unobservable things (like feelings or thoughts). Instead, focus on observable results (things we can see) like measurements and observations from experiments conducted by scientists over time.If your hypothesis isn’t testable, then it needs to be reformulated.

What Should You Do If Your Hypothesis Is Incorrect?

You need to reformulate your thesis if it’s incorrect. You may have to reevaluate the problem or look at it differently. It’s also possible that you need to test your hypothesis with a different method of experimentation.

Here are some ideas from the best scientific thesis writing help experts:

Try Another Approach: Try looking at your hypothesis from a different angle, or consider changing up your methods entirely (for example, instead of asking people what they think will happen in the future and then testing their opinions against reality, you could run an experiment where participants predict events and then actually follow up on those predictions). Share Your Idea with a Third Party: Your hypothesis can be tested by allowing a third party to observe the results of your attempt to prove or disprove the statement. For example, if you’re testing whether peanuts can be made into peanut butter using only as few steps as possible, have someone else make it for you or observe them make it.

Document how you made your product and recorded any necessary changes along the way. This will help you know what works and doesn’t so that you’ll make changes to the whole idea.

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Evolving the Scaled Agile Framework:

Update to SAFe 5

Guidance for organizing around value, DevSecOps, and agility for business teams

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SAFe Glossary

The SAFe glossary is a set of definitions for all SAFe Big Picture elements.  The extended glossary provides definitions for additional terms used in the Framework. Some are unique to SAFe (e.g., PO Sync), while others are common in Lean-Agile development (e.g., MVP). They are provided here for clarity in their meaning in the context of SAFe. All extended glossary terms appear in the English configuration and will appear in other language configurations once translated.

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The 5 Whys is a proven problem-solving technique used to explore the cause-and-effect relationships underlying a particular problem as part of Inspect and Adapt.

Acceptance Criteria

Acceptance Criteria provide the information needed to ensure that a story, feature, or capability is implemented correctly and covers the relevant functionality and NFRs.

Acceptance Test Driven Development

Acceptance Test-Driven Development is a test-first, Agile testing practice synonymous with Behavior-Driven Development (BDD).

Agile is a set of values, principles, and practices for iterative development most notably described by the Agile Manifesto.

Agile Manifesto

The Agile Manifesto is the seminal Agile document describing the four values and twelve principles of Agile software development.

Agile Product Delivery

Agile Product Delivery is a customer-centric approach to defining, building, and releasing a continuous flow of valuable products and services to customers and users.

Agile Program Management Office

The Agile Program Management Office (APMO) is an organizational function responsible for facilitating the Lean Portfolio Management process and fostering operational excellence and lean governance as part of a Lean-Agile transformation.

Agile Release Train (ART)

The Agile Release Train (ART) is a long-lived team of Agile teams, which, along with other stakeholders, incrementally develops, delivers, and where applicable operates, one or more solutions in a value stream.

Agile Teams

In SAFe, an Agile team is a cross-functional group of 5-11 individuals who define, build, test, and deliver an increment of value in a short time box.

Architect Sync

The Architect Sync is a Solution Train event to ensure consistency in how emerging designs and tradeoffs are managed across the Solution Train, allowing frequent opportunities to steer implementation approaches without becoming a source of delays.

Architectural Runway

The Architectural Runway consists of the existing code, components, and technical infrastructure needed to implement near-term features without excessive redesign and delay.

The ART Sync is an ART event that combines the Product Owner (PO) Sync and Scrum of Scrums (SoS).

Backlog Refinement

Backlog Refinement is an activity held once or twice during the iteration or increment to discuss, estimate, and establish an initial understanding of acceptance criteria for upcoming stories in the team’s backlog.

Baseline Solution Investments (BSIs)

Baseline Solution Investments (BSIs) are those costs incurred by each value stream as it develops, supports, and operates the solutions that deliver current business capabilities.

Batch size is a measure of how much work (the requirements, designs, code, tests, and other work items) is pulled into the system during any given timebox.

Behavior-Driven Development

Behavior-Driven Development (BDD) is a test-first, Agile testing practice that provides built-in quality by defining (and potentially automating) tests before, or as part of, specifying system behavior.

Benefit Hypothesis

The Benefit Hypothesis is the proposed measurable benefit to the end-user or business as part of a feature or capability.

Big Visible Information Radiator (BVIR)

A Big Visible Information Radiator (BVIR) is a graphical display that tracks and communicates critical data at a glance (e.g. burndown charts, program board, build status boards).

Built-In Quality

Built-In Quality practices ensure that each Solution element, at every increment, meets appropriate quality standards throughout development.

Burn-Down (Burn-Up) Chart

Burn-Down and Burn-Up Charts are graphical displays that show work progress versus time.

Business Agility

Business Agility is the ability to compete and thrive in the digital age by quickly responding to market changes and emerging opportunities with innovative, digitally-enabled business solutions.

Business and Technology

The Business and Technology icon in SAFe describes how functional domains in all parts of the enterprise enable business agility by continuously exploring new ways to apply Lean-Agile principles and practices to their unique contexts.

Business Context

Business Context is a PI Planning agenda item presented by a business owner that describes the current state of the business, shares the portfolio vision, and presents a perspective on how effectively existing solutions are addressing current customer needs.

Business Owners

Business Owners are a small group of stakeholders who have the primary business and technical responsibility for governance, compliance, and return on investment (ROI) for a Solution developed by an Agile Release Train (ART). They are key stakeholders on the ART who must evaluate fitness for use and actively participate in certain ART events.

SAFe’s CALMR approach to DevOps is a mindset that guides ARTs toward achieving continuous value delivery by managing simultaneous advancements in delivery culture, automation, lean flow, measurement, and recovery.

Capabilities

A Capability is a higher-level solution behavior that typically spans multiple ARTs. Capabilities are sized and split into multiple features to facilitate their implementation in a single PI.

Capacity Allocation

Capacity Allocation is a lean budgeting guardrail for backlogs that helps balance the backlog of new features, enablers, and technical debt allocated to for upcoming Program Increment (PI).

Committed PI Objectives

The Committed PI Objectives are a set of SMART objectives that are created by each team with the business value assigned by the Business Owners.

Communities of Practice (CoPs)

Communities of Practice (CoPs) are organized groups of people who have a common interest in a specific technical or business domain. They collaborate regularly to share information, improve their skills, and actively work on advancing the general knowledge of the domain.

Compliance refers to a strategy and a set of activities and artifacts that allow teams to apply Lean-Agile development methods to build systems that have the highest possible quality, while simultaneously ensuring they meet any regulatory, industry, or other relevant standards.

Confidence Vote

The Confidence Vote is held near the end of PI Planning where the teams vote on their confidence in meeting PI objectives.

Continuous Delivery Pipeline (CDP)

The Continuous Delivery Pipeline (CDP) represents the workflows, activities, and automation needed to shepherd a new piece of functionality from ideation to an on-demand release of value to the end user.

Continuous Deployment (CD)

Continuous Deployment (CD) is the process that takes validated Features in a staging environment and deploys them into the production environment, where they are readied for release.

Continuous Exploration (CE)

Continuous Exploration (CE) is the process that drives innovation and fosters alignment on what should be built by continually exploring market and customer needs, and defining a Vision, Roadmap, and set of Features for a Solution that addresses those needs.

Continuous Integration (CI)

Continuous Integration (CI) is the process of taking features from the Program Backlog and developing, testing, integrating, and validating them in a staging environment where they are ready for deployment and release.

Continuous Learning Culture

The Continuous Learning Culture competency describes a set of values and practices that encourage individuals—and the enterprise as a whole—to continually increase knowledge, competence, performance, and innovation.

Core Values

The four Core Values of alignment, built-in quality, transparency, and program execution represent the fundamental beliefs that are key to SAFe’s effectiveness. These guiding principles help dictate behavior and action for everyone who participates in a SAFe portfolio.

Cost of Delay

Cost of Delay (CoD) represents the money or value that will be lost by delaying or not doing a job for some time and is used in WSJF prioritization.

Customers are the ultimate beneficiaries of the value of the business solutions created and maintained by the portfolio value streams.

Customer Centricity

Customer centricity is a mindset and a way of doing business that focuses on creating positive experiences for the customer through the full set of products and services that the enterprise offers.

Customer Journey Map

A Customer Journey Map illustrates the experiences as a user engages with a company’s operational value stream, products, and services.

Daily Stand-Up

The Daily Stand Up (DSU) is a daily team event where each team member describes what they did yesterday to advance iteration goals, what they are going to work on today to achieve the iteration goals, and any blocks they are encountering in delivering iteration goals.

Decentralized Decision-Making

Decentralized Decision-Making grants decision authority to those closest to the knowledge and information to reduce delays, increase product development flow, and improve the quality of decisions.

Definition of Done

The Definition of Done communicates the completeness for an increment of value and creates a shared understanding of what work was completed as part of an Increment.

To deploy is to migrate a change from a pre-production environment to a production (or operational) environment, where it then awaits release.

Design Thinking

Design Thinking is a customer-centric development process that creates desirable products that are profitable and sustainable over their lifecycle.

Develop on Cadence

Develop on Cadence is a coordinated set of practices that support Agile Teams by providing a reliable series of events and activities that occur on a regular, predictable schedule.

Development Value Streams

Development value streams (DVS) are the sequence of activities needed to convert a business hypothesis into a digitally-enabled Solution. Examples include designing a medical device or geophysical satellite, or developing and deploying a software application, SaaS system, or an e-commerce web site.

DevOps is a mindset, a culture, and a set of technical practices. It provides communication, integration, automation, and close cooperation among all the people needed to plan, develop, test, deploy, release, and maintain a Solution.

Empathy Map

An Empathy Map is a design thinking tool that helps teams develop deep, shared understanding for their customers.

An Enabler supports the activities needed to extend the Architectural Runway to provide future business functionality. These include exploration, architecture, infrastructure, and compliance. Enablers are captured in the various backlogs and occur throughout the Framework.

The Enterprise represents the business entity to which each SAFe portfolio belongs.

Enterprise Architect

The Enterprise Architect establishes a technology strategy and roadmap that enables a portfolio to support current and future business capabilities.

Enterprise Solution Delivery

The Enterprise Solution Delivery competency describes how to apply Lean-Agile principles and practices to the specification, development, deployment, operation, and evolution of the world’s largest and most sophisticated software applications, networks, and cyber-physical systems.

Epic Hypothesis Statement

The Epic Hypothesis Statement captures, organizes, and communicates critical information about an epic.

Epic Owners

Epic Owners are responsible for coordinating portfolio Epics through the Portfolio Kanban system. They collaboratively define the epic, its Minimum Viable Product (MVP), and Lean business case, and when approved, facilitate implementation.

An Epic is a container for a significant Solution development initiative that captures the more substantial investments that occur within a portfolio. Due to their considerable scope and impact, epics require the definition of a Minimum Viable Product (MVP) and approval by Lean Portfolio Management (LPM) before implementation.

Essential SAFe

Essential SAFe contains the minimal set of roles, events, and artifacts required to continuously deliver business solutions via an Agile Release Train (ART) as a Team of Agile Teams.

Estimating Poker

Estimating Poker is a collaborative technique for relatively estimating the size of stories, features, and WSJF in SAFe.

Extreme Programming

Extreme Programming (XP) is a set of Agile software engineering practices that improves software quality and responsiveness to changing customer requirements developed primarily by Kent Beck.

A Feature is a service that fulfills a stakeholder need. Each feature includes a benefit hypothesis and acceptance criteria, and is sized or split as necessary to be delivered by a single Agile Release Train (ART) in a Program Increment (PI).

Final Plan Review

In the Final Plan Review activity of PI planning, teams present the final plans (PI objectives, load, risks) for communication to the ART and acceptance by the Business Owners.

The Foundation contains the supporting principles, values, mindset, implementation guidance, and leadership roles needed to deliver value successfully at scale.

Full SAFe is the most comprehensive configuration, including all seven core competencies needed for business agility.

Gemba is the place where the work is performed and where teams can observe how stakeholders execute the steps and specific activities in their operational value streams to better identify opportunities for relentless improvement.

Hackathons are innovation events where team members can work on whatever they want, with whomever they want, so long as the work reflects the mission of the company and they demo their work to others at the end of the Hackathon.

Innovation and Planning Iteration

The Innovation and Planning (IP) Iteration occurs every Program Increment (PI) and serves multiple purposes. It acts as an estimating buffer for meeting PI Objectives and provides dedicated time for innovation, continuing education, PI Planning, and Inspect and Adapt (I&A) events.

Inspect & Adapt (I&A)

The Inspect and Adapt (I&A) is a significant event, held at the end of each Program Increment (PI), where the current state of the Solution is demonstrated and evaluated by the train. Teams then reflect and identify improvement backlog items via a structured, problem-solving workshop.

Integration Point

An Integration Point creates a ‘pull event’ that pulls the various solution elements into an integrated whole that helps stakeholders assure that the evolving solution address real and future business needs

Investment Horizons

Investment Horizons highlight spending allocations for solutions that are created by the value streams that help value stream owners and fiduciaries make more informed investment decisions and aligns the portfolio with strategic themes while promoting overall health and growth.

Iterations are the basic building block of Agile development. Each iteration is a standard, fixed-length timebox, where Agile Teams deliver incremental value in the form of working, tested software and systems. In SAFe, iterations are typically one or two weeks in length, with two being the most common.

Iteration Execution

Iteration Execution is how Agile Teams manage their work throughout the Iteration timebox, resulting in a high-quality, working, tested system increment.

Iteration Goals

Iteration Goals are a high-level summary of the business and technical goals that the Agile Team agrees to accomplish in an Iteration. They are vital to coordinating an Agile Release Train (ART) as a self-organizing, self-managing team of teams.

Iteration Planning

Iteration Planning is an event where all team members determine how much of the Team Backlog they can commit to delivering during an upcoming Iteration. The team summarizes the work as a set of committed Iteration Goals.

Iteration Retrospective

The Iteration Retrospective is a regular event where Agile Team members discuss the results of the Iteration, review their practices, and identify ways to improve.

Iteration Review

The Iteration Review is a cadence-based event, where each team inspects the increment at the end of every Iteration to assess progress, and then adjusts its backlog for the next iteration.

Knowledge Worker

Knowledge Workers are people who have the skill, expertise, and education needed to solve complex problems in their domain of concern.

Large Solution SAFe

Large Solution SAFe describes additional roles, practices, and guidance to build and evolve the world’s largest applications, networks, and cyber-physical systems.

Lead Time is the time it takes from when the work was done in the previous step until it’s done in the current step.

Lean is a body of knowledge and set of practices to improve efficiency and effectiveness by reducing delays and eliminating non-value-added activities.

Lean Budget Guardrails

Lean Budget Guardrails describe the policies and practices for budgeting, spending, and governance for a specific portfolio.

Lean Budgets

Lean Budgets is a Lean-Agile approach to financial governance which increases throughput and productivity by reducing the overhead and costs associated with project cost accounting.

Lean Business Case

A Lean Business Case (LBC) is a light-weight approach to describing epics, including their MVPs and projected business value.

Lean Governance

Lean Governance is one dimension of Lean Portfolio Management that supports oversight and decision-making of spending, audit and compliance, forecasting expenses, and measurement.

Lean Portfolio Management

The Lean Portfolio Management competency aligns strategy and execution by applying Lean and systems thinking approaches to strategy and investment funding, Agile portfolio operations, and governance.

Lean Quality Management System (QMS)

A Quality Management System (QMS) dictates practices, policies, and procedures needed to confirm safety and efficacy. SAFe organizations move from traditional to Lean QMS governance.

Lean User Experience (Lean UX)

Lean User Experience (Lean UX) design is a mindset, culture, and a process that embraces Lean-Agile methods. It implements functionality in minimum viable increments and determines success by measuring results against a benefit hypothesis.

Lean-Agile Center of Excellence

The Lean-Agile Center of Excellence (LACE) is a small team of people dedicated to implementing the SAFe Lean-Agile way of working.

Lean-Agile Leadership

The Lean-Agile Leadership competency describes how Lean-Agile Leaders drive and sustain organizational change and operational excellence by empowering individuals and teams to reach their highest potential.

Lean-Agile Mindset

The Lean-Agile Mindset is the combination of beliefs, assumptions, attitudes, and actions of SAFe leaders and practitioners who embrace the concepts of the Agile Manifesto and Lean thinking. It’s the personal, intellectual, and leadership foundation for adopting and applying SAFe principles and practices.

Lean-Agile Principles

SAFe is based on ten immutable, underlying Lean-Agile principles. These tenets and economic concepts inspire and inform the roles and practices of SAFe.

Little’s Law

Little’s Law is the law of queuing theory and states that the average wait time for service from a system equals the ratio of the average queue length divided by the average processing rate.

Measure and Grow

Measure and Grow is the way portfolios evaluate their progress towards business agility and determine their next improvement steps.

Metrics are agreed-upon measures used to evaluate how well the organization is progressing toward the portfolio, large solution, ART, and Agile team’s business and technical objectives.

Milestones are used to track progress toward a specific goal or event. There are three types of SAFe milestones: Program Increment (PI), fixed-date, and learning milestones.

Minimum Marketable Feature

The Minimum Marketable Feature (MMF) is the minimum functionality that the teams can build to learn whether the feature’s benefit hypothesis is valid or not.

Minimum Viable Product

In SAFe, a Minimum Viable Product (MVP) is an early and minimal version of a new product or business solution that is used to prove or disprove the epic hypothesis. As opposed to storyboards, prototypes, mockups, wireframes, and other exploratory techniques, the MVP is an actual product used by real customers to generate validated learning.

Model-Based Systems Engineering (MBSE)

Model-Based Systems Engineering (MBSE) is the practice of developing a set of related system models that help define, design, and document a system under development. These models provide an efficient way to explore, update, and communicate system aspects to stakeholders, while significantly reducing or eliminating dependence on traditional documents.

Modified Fibonacci Sequence

A Modified Fibonacci Sequence (1, 2, 3, 5, 8, 13, 20, 40, 100) is used during relative estimating to reflect the inherent uncertainty as the size of the job being estimated gets bigger.

Nonfunctional Requirements (NFRs)

Nonfunctional Requirements (NFRs) define system attributes such as security, reliability, performance, maintainability, scalability, and usability. They serve as constraints or restrictions on the design of the system across the different backlogs.

Objectives and Key Results

In SAFe, Objectives and Key Results (OKRs) can be used to define, organize, and communicate critical information about a strategic theme and track its progress through concrete, specific, and measurable actions.

Operational Value Streams

Operational value streams (OVS) are the sequence of activities needed to deliver a product or service to a customer. Examples include manufacturing a product, fulfilling an order, admitting and treating a medical patient, providing a loan, or delivering a professional service.

Organizational Agility

The Organizational Agility competency describes how Lean-thinking people and Agile teams optimize their business processes, evolve strategy with clear and decisive new commitments, and quickly adapt the organization as needed to capitalize on new opportunities.

Organizational Change Management

Organizational Change Management is a collective term for all approaches to prepare, support, and help individuals, teams, and organizations in making organizational change.

Pareto Analysis

Pareto Analysis is a technique used during an Inspect & Adapt event to narrow down the number of actions that produce the most significant overall effect.

Participatory Budgeting

Participatory Budgeting (PB) is the process that Lean Portfolio Management (LPM) uses to allocate the total portfolio budget to its value streams.

Personas are fictional consumers and/or users derived from customer research that drive a customer-centric approach to product development.

Phase Gates are traditional governance milestones that are replaced in SAFe by milestones based on objective evaluation of working systems.

PI Objectives

Program Increment (PI) Objectives are a summary of the business and technical goals that an Agile Team or train intends to achieve in the upcoming Program Increment (PI).

Plan-Do-Check-Adjust

Plan-Do-Check-Adjust (PDCA) is an iterative, four-step method used for controlling variability and making adjustments in response to feedback during product development.

A SAFe portfolio aligns strategy to execution via a collection of development value streams. Operating under a common governance model, each value stream provides one or more solutions the enterprise needs to accomplish its business mission.

Portfolio Backlog

The Portfolio Backlog is the highest-level backlog in SAFe. It provides a holding area for upcoming business and enabler Epics intended to create and evolve a comprehensive set of Solutions.

Portfolio Canvas

The Portfolio Canvas defines the development value streams that are included in a SAFe portfolio, the value propositions and the solutions they deliver, the customers they serve, the budgets allocated to each value stream, and other key activities and events required to achieve the portfolio vision.

Portfolio Kanban

The Portfolio Kanban system is a method to visualize and manage the flow of portfolio Epics, from ideation through analysis, implementation, and completion.

Portfolio SAFe

Portfolio SAFe aligns strategy with execution and organizes solution development around the flow of value through one or more value streams.

Portfolio Vision

The Portfolio Vision is a description of the future state of a portfolio’s Value Streams and Solutions and describes how they will cooperate to achieve the portfolio’s objectives and the broader aim of the Enterprise.

Pre-and Post-PI Planning

Pre– and Post–Program Increment (PI) Planning events are used to prepare for, and follow up after, PI Planning for Agile Release Trains (ARTs) and Suppliers in a Solution Train.

Problem-Solving Workshop

The Problem Solving Workshop is part of the Inspect and Adapt (I&A) event and is a structured approach to finding the root cause of systemic problems.

Product Management

Product Management is responsible for defining desirable, viable, feasible, and sustainable solutions that meet customer needs and for supporting development across the entire product life cycle.

Product Owner (PO)

The Product Owner (PO) is a member of the Agile Team who is responsible for maximizing the value delivered by the team and ensuring that the Team Backlog is aligned with customer and stakeholder needs.

Product Owner (PO) Sync

The PO Sync is an ART event to get visibility into how well the ART is progressing toward meeting its PI objectives, to discuss problems or opportunities with feature development, and to assess any scope adjustments.

Program Backlog

The Program Backlog is the holding area for upcoming Features, which are intended to address user needs and deliver business benefits for a single Agile Release Train (ART). It also contains the enabler features necessary to build the Architectural Runway.

Program Board

The Program Board highlights the PI’s feature delivery dates, feature dependencies among teams, and relevant milestones.

Program Increment (PI)

A Program Increment (PI) is a timebox during which an Agile Release Train (ART) delivers incremental value in the form of working, tested software and systems. PIs are typically 8 – 12 weeks long. The most common pattern for a PI is four development Iterations, followed by one Innovation and Planning (IP) Iteration.

Program Increment (PI) Planning

Program Increment (PI) Planning is a cadence-based, face-to-face event that serves as the heartbeat of the Agile Release Train (ART), aligning all the teams on the ART to a shared mission and Vision.

Program Kanban

The Program and Solution Kanban systems are a method to visualize and manage the flow of Features and Capabilities from ideation to analysis, implementation, and release through the Continuous Delivery Pipeline.

Program Predictability Measure

The Program Predictability Measure summarizes the planned vs. actual business values for all the teams on the ART and is a key indicator of the ART’s performance and reliability.

Program Risks

Program Risks are identified by the teams during PI Planning and represent risks and impediments that could impact their ability to meet their objectives.

Refactoring

Refactoring is the activity of improving the internal structure or operation of a code or component without changing its external behavior.

Relative Estimation

Relative Estimation compares jobs to one another to quickly estimate their size and value.

To release is to make functionality that has been deployed to a production (or operational) environment available for use by a defined set of end-users or systems.

Release on Demand

Release on Demand is the process that deploys new functionality into production and releases it immediately or incrementally to customers based on demand.

Release Train Engineer (RTE)

The Release Train Engineer (RTE) is a servant leader and coach for the Agile Release Train (ART). The RTE’s major responsibilities are to facilitate the ART events and processes and assist the teams in delivering value. RTEs communicate with stakeholders, escalate impediments, help manage risk, and drive relentless improvement.

Relentless Improvement

Relentless Improvement is the fourth pillar of the SAFe House of Lean and encourages learning and growth through continuous reflection and process enhancements.

The Roadmap is a schedule of events and Milestones that communicate planned Solution deliverables over a planning horizon.

ROAMing Risks

Risk ROAMing is a PI planning activity where program risks raised by teams are addressed in a broader management context.

Root Cause Analysis

A Root Cause Analysis applies a set of problem-solving tools to identify the actual causes of a problem as part of the Inspect and Adapt event.

SAFe Big Picture (BP)

The SAFe Big Picture (BP) is a visual representation of the framework’s primary roles, activities, and artifacts used to access SAFe articles through its clickable icons when viewed from scaledagileframework.com

SAFe for Government

SAFe for Government is a set of success patterns that help public sector organizations implement Lean-Agile practices in a government context.

SAFe for Lean Enterprises

SAFe for Lean Enterprises is the world’s leading framework for business agility. SAFe integrates the power of Lean, Agile, and DevOps into a comprehensive operating system that helps enterprises thrive in the digital age by delivering innovative products and services faster, more predictably, and with higher quality.

The SAFe Implementation Roadmap consists of an overview graphic and a 12-article series that describes a strategy and an ordered set of activities that have proven to be effective in successfully implementing SAFe.

SAFe Lean Startup Cycle

The SAFe Lean Startup cycle is a highly iterative build-measure-learn cycle for product innovation and strategic investments. This strategy for implementing epics provides the economic and strategic advantages of a Lean startup by managing investment and risk incrementally while leveraging the flow and visibility benefits of SAFe.

SAFe Program Consultants (SPCs)

Certified SAFe® Program Consultants (SPCs) are change agents who combine their technical knowledge of SAFe with an intrinsic motivation to improve the company’s software and systems development processes. They play a critical role in successfully implementing SAFe. SPCs come from numerous internal or external roles, including business and technology leaders, portfolio/program/project managers, process leads, architects, analysts, and consultants.

Scrum Master

SAFe Scrum Masters are servant leaders and coaches for an Agile Team. They help educate the team in Scrum, Extreme Programming (XP), Kanban, and SAFe, ensuring that the agreed Agile process is followed. They also help remove impediments and foster an environment for high-performing team dynamics, continuous flow, and relentless improvement.

Scrum of Scrums

The Scrum of Scrums (SoS) is an ART event that helps coordinate ART dependencies and provides visibility into progress and impediments.

SAFe ScrumXP is an Agile Team method used by Agile Release Trains (ARTs) to plan, execute, retrospect, and deliver customer value in a short time box. It combines the power of Scrum with Extreme Programming (XP) practices.

Set-Based Design

Set-Based Design (SBD) is a practice that keeps requirements and design options flexible for as long as possible during the development process. Instead of choosing a single point solution upfront, SBD identifies and simultaneously explores multiple options, eliminating poorer choices over time. It enhances flexibility in the design process by committing to technical solutions only after validating assumptions, which produces better economic results.

Shared Services

Shared Services represents the specialty roles, people, and services required for the success of an Agile Release Train (ART) or Solution Train, but that cannot be dedicated full-time.

Silos are functionally-aligned organizational constructs that locally optimize for specialists with policies and procedures that ensure repeatable, efficient operations within the functional unit without understanding the larger flow of value across functional units.

Each development value stream develops one or more Solutions, which are products, services, or systems delivered to the customer, whether internal or external to the Enterprise.

Solution Architect/Engineering

Solution Architect/Engineering is responsible for defining and communicating a shared technical and architectural vision across a Solution Train to help ensure the system or Solution under development is fit for its intended purpose.

Solution Backlog

The Solution Backlog is the holding area for upcoming Capabilities and Enablers, each of which can span multiple ARTs and is intended to advance the Solution and build its architectural runway.

Solution Context

Solution Context identifies critical aspects of the operational environment for a Solution. It provides an essential understanding of requirements, usage, installation, operation, and support of the solution itself. Solution context heavily influences opportunities and constraints for releasing on demand.

Solution Demo

The Solution Demo integrates the development efforts from all ARTs and suppliers on the Solution Train every PI and makes them visible to Customers and other stakeholders for evaluation and feedback.

Solution Intent

Solution Intent is the repository for storing, managing, and communicating the knowledge of current and intended Solution behavior. Where required, this includes both fixed and variable specifications and designs; reference to applicable standards, system models, and functional and nonfunctional tests; and traceability.

Solution Management

Solution Management is responsible for defining and supporting the building of desirable, feasible, viable and sustainable large scale business solutions that meet customer needs over time.

Solution Train

The Solution Train is the organizational construct used to build large and complex Solutions that require the coordination of multiple Agile Release Trains (ARTs), as well as the contributions of Suppliers. It aligns ARTs with a shared business and technology mission using the solution Vision, Backlog, and Roadmap, and an aligned Program Increment (PI).

Solution Train Engineer (STE)

The Solution Train Engineer (STE) is a servant leader and coach for the Solution Train, facilitating and guiding the work of all ARTs and Suppliers in the Value Stream.

Spanning Palette

The Spanning Palette contains various roles and artifacts that may apply to a specific team, program, large solution, or portfolio context.

A Spike is a type of exploration Enabler Story that gains the knowledge necessary to reduce the risk of a technical approach, better understand a requirement, or increase the reliability of a story estimate.

Sprint is a term that comes from the Scrum method and is synonymous with the term Iteration in SAFe.

Stories are short descriptions of a small piece of desired functionality, written in the user’s language. Agile Teams implement small, vertical slices of system functionality and are sized so they can be completed in a single Iteration.

A Story Map is a design thinking technique that organizes a sequence of stories according to the tasks a user needs to accomplish their goal.

Story Point

A Story Point is a singular number used in relative estimating that represents a combination of quantities: volume, complexity, knowledge, and uncertainty.

Strategic Themes

Strategic Themes are differentiating business objectives that connect a portfolio to the strategy of the Enterprise. They influence portfolio strategy and provide business context for portfolio decision-making.

Sunk Costs refers to money already spent, which should be ignored when making future investment decisions to pivot effectively.

A Supplier is an internal or external organization that develops and delivers components, subsystems, or services that help Solution Trains and Agile Release Trains provide Solutions to their Customers.

SWOT Analysis

SWOT Analysis is a strategic planning technique used to identify strengths, weaknesses, opportunities, and threats related to the current business situation as part of a SAFe portfolio vision.

System Architect/Engineering

System Architect/Engineering is responsible for defining and communicating a shared technical and architectural vision for an Agile Release Train (ART) to help ensure the system or Solution under development is fit for its intended purpose.

System Demo

The System Demo is a significant event that provides an integrated view of new Features for the most recent Iteration delivered by all the teams in the Agile Release Train (ART). Each demo gives ART stakeholders an objective measure of progress during a Program Increment (PI).

System Team

The System Team is a specialized Agile Team that assists in building and supporting the Agile development environment, typically including development and maintenance of the toolchain that supports the Continuous Delivery Pipeline. The System Team may also support the integration of assets from Agile teams, perform end-to-end Solution testing where necessary, and assists with deployment and Release on Demand.

Systems Thinking

Systems Thinking takes a holistic approach to solution development, incorporating all aspects of a system and its environment into the design, development, deployment, and maintenance of the system itself.

Team and Technical Agility

The Team and Technical Agility competency describes the critical skills and Lean-Agile principles and practices that high-performing Agile teams and Teams of Agile teams use to create high-quality solutions for their customers.

Team Backlog

The Team Backlog contains user and enabler Stories that originate from the Program Backlog, as well as stories that arise locally from the team’s local context. It may include other work items as well, representing all the things a team needs to do to advance their portion of the system.

Team Kanban

Team Kanban is a method that helps teams facilitate the flow of value by visualizing workflow, establishing Work In Process (WIP) limits, measuring throughput, and continuously improving their process.

Team Topologies

Team Topologies define four organization types that provide a clear model for organizing Agile teams and ARTs.

Technical Debt

Technical Debt reflects the implied cost and accumulating interest of future work that is commonly caused by knowingly or unknowingly choosing a suboptimal or incomplete solution.

Test-Driven Development

Test-Driven Development (TDD) is a mindset and practice that involves building and executing tests before implementing the code or a component of a system.

TOWs Analysis

TOWS Analysis is used in conjunction with a SWOT analysis to help identify strategic options to create a better future state as part of a SAFe portfolio vision.

U-curve Optimization

The U-curve Optimization for batch size determines the optimal batch size by balancing transaction costs and holding costs.

Uncommitted Objectives

Uncommitted Objectives help improve the predictability of delivering business value since they are not included in the team’s commitment or counted against teams in the program predictability measure. Teams can apply uncommitted objectives whenever there is low confidence in meeting the objective.

Value represents the benefits an enterprise delivers to its customers and stakeholders and appears in different contexts in SAFe.

Value Stream Coordination

Value Stream Coordination defines how to manage dependencies and exploit the opportunities that exist only in the interconnections between value streams.

Value Stream Identification

Value Stream Identification is an activity that portfolios use to identify development value streams and the operational value streams they support.

Value Stream KPIs

Value Stream Key Performance Indicators (KPIs) are the quantifiable measures used to evaluate how a value stream is performing against its forecasted business outcomes.

Value Stream Management (VSM)

Value Stream Management is a leadership and technical discipline that enables maximum flow of business value through the end-to-end solution delivery life cycle.

Value Stream Mapping

Value Stream Mapping is an essential tool to improve the flow of value across the continuous delivery pipeline by providing the visibility needed to identify bottlenecks and problem areas to flow that cause delays.

Value Streams

Value Streams represent the series of steps that an organization uses to implement Solutions that provide a continuous flow of value to a customer.

Velocity is equal to the sum of the points for all the completed stories that met their Definition of Done (DoD).

The Vision is a description of the future state of the Solution under development. It reflects customer and stakeholder needs, as well as the Feature and Capabilities proposed to meet those needs.

Weighted Shortest Job First (WSJF)

Weighted Shortest Job First (WSJF) is a prioritization model used to sequence jobs (for example, Features, Capabilities, and Epics) to produce maximum economic benefit. In SAFe, WSJF is estimated as the Cost of Delay (CoD) divided by the job duration.

Work in Process

Work in Process (WIP) represents partially completed work. Having too much WIP confuses priorities, causes frequent context switching, and increases overhead.

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Strategic Team Building | Benefits Of Hypothesis For Business

By Joe Ferraro | 11 September, 2024

In today’s competitive business environment, teamwork is the key to success. Teams can work together smoothly and effectively by using Hypothesis, a simple yet powerful way to explore ideas and solve problems. By combining Hypothesis with business collaboration tools , teams can communicate better, make smarter decisions, and drive innovation.

In this blog, we’ll explore practical steps and examples that show how applying Hypothesis for  business , supported by the right tools, can lead to successful business strategies and improved team dynamics. Get ready to change the way you think about teamwork and tools in business!

What Is Hypothesis for Business ? 

A hypothesis business is similar to one in science, it’s a guess or prediction made based on existing knowledge that can be tested through further investigation and analysis. In a business context, Hypothesis help teams focus on potential outcomes and test different strategies before fully committing resources. This method helps a culture of evidence-based decision-making and reduces the risk associated with new initiatives.

Hypothesis encourage team members to think critically and test their assumptions, ensuring that every decision is backed by thoughtful consideration and data. Meanwhile, business collaboration tools provide the necessary platform for sharing these ideas and insights, keeping everyone on the same page. Whether you’re working on a small project or a major enterprise, integrating these strategies can transform the way your team operates, making collaboration seamless and more productive.

The Importance Of Hypothesis In Strategic Team Building

• Encourages A Proactive Approach : The hypothesis compels team members to think ahead, anticipate potential outcomes, and prepare for obstacles, promoting a proactive work culture essential in today’s advanced business environment.
• Promote Innovation : By questioning assumptions and validating ideas through systematic testing, hypothesis encourage creative problem-solving and innovation, allowing teams to explore a wide range of possibilities.
• Reduces Waste : Hypothesis helps minimize the wastage of resources, such as time and money, by identifying less viable options early, ensuring that only the most promising ideas are pursued.
• Enhances Learning And Adaptation : Testing hypothesis provides valuable insights, into whether outcomes validate or refute the initial assumptions, which is important for continuous improvement and adaptation to ever-changing business dynamics.
• Promotes Risk Management : The hypothesis allows teams to assess risks in a controlled environment, making it possible to manage potential downsides more effectively before full-scale implementation.
• Improves Decision-Making Quality : The structured approach of hypothesis promotes data-driven decision-making, which tends to be more accurate and effective, leading to better outcomes for the business.
• Strengthens Team Collaboration : As teams come together to formulate, test, and revise hypothesis, the collaborative process is strengthened. This unity helps in building a cohesive team capable of tackling complex projects.
• Clarifies Objectives And Focus : Working with hypothesis requires clear objectives and a focused approach, which helps teams stay aligned with the business’s overall goals and ensures that everyone is working towards the same end.
• Increases Accountability : When teams operate under hypothesis, each member becomes accountable for contributing to the testing and proving of these assumptions, helping a sense of responsibility and ownership.
• Enables Scalability : Hypothesis can be scaled across different teams and departments, allowing successful strategies to be replicated and adapted throughout the organization, leading to overall improvement and efficiency.

Leveraging Business Collaboration Tools

To effectively test and implement hypothesis, businesses must utilize collaboration tools. These tools not only facilitate communication but also ensure that all team members are aligned and informed. Some of the key functionalities of these tools include:

• Document Sharing And Management : Tools like Google Drive and Microsoft OneDrive allow teams to store, share, and edit documents in real-time, ensuring everyone has access to the latest information.
• Communication Platforms : Slack and Microsoft Teams enable real-time messaging, video calls, and team meetings, making it easier to discuss hypothesis and updates without delay.
• Project Management Software : Asana and Trello provide platforms for tracking project progress, assigning tasks, and setting deadlines. These tools help in organizing the testing of hypothesis and monitoring outcomes.
• Data Analysis Tools : Software like Tableau and Microsoft Power BI helps in analyzing data to test hypotheses. These tools can transform raw data into actionable insights, supporting evidence-based decision-making.

Integrating Hypothesis With Collaboration Tools

The integration of Hypothesis and collaboration tools can be transformative, but it requires a structured approach. Here’s how businesses can effectively combine these elements:

• Define Clear Objectives : Start by clearly defining what you want to achieve with your hypothesis. This clarity will guide the testing process and the use of tools.
• Develop A Testable Hypothesis : Ensure the hypothesis is specific, measurable, achievable, relevant, and time-bound (SMART). This specificity will help in the accurate testing and analysis of results.
• Select Appropriate Tools : Choose collaboration tools that best fit your team’s needs and the specifics of the hypothesis being tested. Consider factors like usability, scalability, and integration capabilities.
• Conduct Tests And Collect Data : Use the selected tools to conduct experiments and collect data. Ensure all team members know their roles and how to use the tools effectively to contribute to the process.
• Analyze Results And Make Decisions : Analyze the data to determine whether the hypothesis holds. Use the insights gained to make informed decisions and refine your business strategies.
• Document And Share Learnings : Utilize your collaboration tools to document the outcomes and share learnings with the team. This step ensures that everyone benefits from the experience, regardless of their direct involvement in the testing phase.

Using Hypothesis For Business

The hypothesis for business is a methodical way to approach problem-solving, decision-making, and strategy development. Here’s a step-by-step guide on how to effectively utilize hypothesis in your business processes:

1. Identify The Problem Or Opportunity

Start by clearly defining the problem you want to solve or the opportunity you want to explore. This will form the basis of your hypothesis. It’s essential to be as specific as possible to ensure that your hypothesis is relevant and actionable.

2. Formulate The Hypothesis

A hypothesis is essentially an educated guess about a potential outcome. It should be specific, testable, and based on observations or preliminary data. For example, “Implementing an online ordering system will increase our restaurant’s sales by 20% within the first three months.”

3. Gather Data

Before you can test your hypothesis, gather the necessary data that will inform your experiment. This may involve collecting historical business data, industry benchmarks, or customer feedback relevant to your hypothesis.

4. Design The Experiment

Plan how you will test your hypothesis. This involves setting up the conditions under which you will observe outcomes and decide on the metrics that will indicate success or failure. Ensure that the test is controlled and that variables not related to the hypothesis are minimized.

5. Conduct The Experiment

Implement the changes or introduce the new elements required by your hypothesis. This might involve launching a new marketing campaign, changing a production process, or introducing a new product feature. Monitor the experiment closely to gather real-time data.

6. Analyze The Results

After the experiment, analyze the data to see whether it supports or refutes your hypothesis. Use statistical tools to ensure your findings are valid and reliable. It’s important to remain unbiased and objective during this step.

7. Make Decisions

Based on the results of your hypothesis test, make informed decisions. If the hypothesis is confirmed, consider implementing the change on a larger scale. If it is refuted, use the insights gained to refine your hypothesis or abandon the approach.

8. Refine And Repeat

Whether your hypothesis was confirmed or not, there’s always more to learn. Use the results as a stepping stone to further refine your approach. Adjust your hypothesis based on the insights gained and repeat the process with a new hypothesis to continue optimizing your business operations.

9. Document Everything

Maintain thorough documentation of your hypothesis, the testing process, results, and decisions made. This documentation will help you track progress over time, provide insights into what works and what doesn’t, and inform future hypothesis.

10. Scale And Implement

For a hypothesis that proves successful, consider how to scale the successful initiative. This might involve broader implementation, integrating the change into standard business practices, or investing more resources.

Conclusion 

Incorporating Hypothesis for business practices can lead to more strategic decision-making and innovation. When combined with the right collaboration tools, this approach not only enhances the effectiveness of teamwork but also moves the business toward its objectives. By applying a culture that embraces hypothesis, businesses can ensure that every team action is as informed and impactful as possible.

By adapting to this hypothesis-driven approach, businesses can navigate the complexities of the modern market more effectively, ensuring both short-term success and long-term sustainability.

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USCIS Issues Final Rule to Adjust Certain Immigration and Naturalization Fees

WASHINGTON –  Today, U.S. Citizenship and Immigration Services (USCIS) published a  final rule to adjust certain immigration and naturalization benefit request fees for the first time since 2016. The final rule will allow USCIS to recover a greater share of its operating costs and support more timely processing of new applications.

The final rule is the result of a comprehensive fee review, as required by law, and follows the January 2023 publication of a notice of proposed rulemaking. The review concluded that the current fee schedule falls far short in recovering the full cost of agency operations, including the necessary expansion of humanitarian programs, federally mandated pay raises, additional staffing requirements, and other essential investments.

“For the first time in over seven years, USCIS is updating our fees to better meet the needs of our agency, enabling us to provide more timely decisions to those we serve,” said USCIS Director Ur M. Jaddou. “Despite years of inadequate funding, the USCIS workforce has made great strides in customer service, backlog reduction, implementing new processes and programs, and upholding fairness, integrity, and respect for all we serve.”

USCIS received over 5,400 unique public comments in response to its January 2023 notice of proposed rulemaking. USCIS took into consideration comments and feedback received during the proposed rulemaking process. Acknowledging this feedback from stakeholders, the final fee rule includes several important updates since the initial rulemaking. The final rule:

• Lowers the agency’s required annual cost recovery by $727 million, in part by considering the budget effects of improved efficiency measures;
• Expands fee exemptions for Special Immigrant Juveniles and victims of human trafficking, crime, and domestic violence; U.S. military service members and our Afghan allies; and families pursuing international adoption;
• Provides special fee discounts for nonprofit organizations and small business employers;
• Allows for half-price Employment Authorization Document applications for applicants for adjustment of status and a reduced fee for adjustment of status applicants under the age of 14 in certain situations;
• Expands eligibility for a 50% fee reduction for naturalization applications, available to individuals who can demonstrate household income between 150% and 400% of the Federal Poverty Guidelines; and
• Implements a standard $50 discount for online filers.

Every fee in the final rule is the same or lower than in the proposed rule. For most individual filers, the final rule limits how much newly established fees may increase. Under the final rule, the new fees will not increase by more than 26%, which is equivalent to the increase in the Consumer Price Index since the last fee rule was issued in 2016.

With the new revenues the rule will generate, USCIS will continue using innovative solutions to improve customer experience and stem backlog growth. Although the fee increases announced today will allow USCIS to better offset overall costs, congressional funding continues to be necessary to sustainably and fully address the increased volume of caseloads associated with recent border crossers, including by hiring additional USCIS personnel to help right-size a system that was not built to manage the numbers of cases USCIS receives.

The new fees under the final rule will go into effect on April 1, 2024.

USCIS encourages stakeholders to visit the  Frequently Asked Questions page on its website to view a full list of the revised forms that will go into effect on April 1, 2024, along with the new fees. USCIS will accept prior editions of most forms during a grace period from April 1, 2024, through June 3, 2024. During this grace period, USCIS will accept both previous and new editions of certain forms, filed with the correct fee.

There will be no grace period for the following new forms, however, because they must be revised with a new fee calculation. Filers should click the links below to access a preview version of each new form edition before the April 1, 2024, effective date:

• Form I-129, Petition for a Nonimmigrant Worker ;
• Form I-129 CW, Petition for a CNMI-Only Nonimmigrant Transitional Worker ;
• Form I-140, Immigrant Petition for Alien Workers ;
• Form I-600A, Application for Advance Processing of an Orphan Petition (and supplement 1, 2 and 3); and
• Form I-600, Petition to Classify Orphan as an Immediate Relative.

USCIS will use the postmark date of a filing to determine which form version and fees are correct but will use the receipt date for purposes of any regulatory or statutory filing deadlines.

For more information on USCIS and its programs, please visit  uscis.gov  or follow USCIS on  Twitter ,  Instagram ,  YouTube ,  Facebook  and  LinkedIn .