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Value Hypothesis & Growth Hypothesis: lean startup validation

Posted on September 16, 2021 |

You’ve come up with a fantastic idea for a startup and you need to discuss the hypothesis and its value? But you’re not sure if it’s a viable one or not. What do you do next? It’s essential to get your ideas right before you start developing them. 95% of new products fail in their first year of launch. Or to put it another way, only one in twenty product ideas succeed. In this article, we’ll be taking a look at why it’s so important to validate your startup idea before you start spending a lot of time and money developing it. And that’s where the Lean Startup Validation process gets into, alongside the growth hypothesis and value hypothesis. We’ll also be looking at the questions that you need to ask.

Table of contents

The lean startup validation methodology, the benefits of validating your startup idea, the value hypothesis, the growth hypothesis, recommendations and questions for creating and running a good hypothesis, in conclusion – take the time to validate your product.

What does it mean to validate a lean startup?

Validating your lean startup idea may sound like a complicated process, but it’s a lot simpler than you may think. It may be the case that you were already planning on carrying out some of the work.

Essentially, validating your startup when you check your idea to see if it solves a problem that your prospective customers have. You can do this by creating hypotheses and then carrying out research to see if these hypotheses are true or false. 

The best startups have always been about finding a gap in the market and offering a product or service that solves the problem. For example, take Airbnb . Before Airbnb launched, people only had the option of staying in hotels. Airbnb opened up the hospitality industry, offering cheaper accommodation to people who could not afford to stay inexpensive hotels. 

“Don’t be in a rush to get big. Be in a rush to have a great product” – Eric Ries

Validation is a crucial part of the lean startup methodology, which was devised by entrepreneur Eric Ries. The lean startup methodology is all about optimizing the amount of time that is needed to ensure a product or service is viable. 

Lean Startup Validation is a critical part of the lean startup process as it helps make sure that an idea will be successful before time is spent developing the final product.

As an example of a failed idea where more validation could have helped, take Google Glass . It sounded like a good idea on paper, but the technology failed spectacularly. Customer research would have shown that $1,500 was too much money, that people were worried about health and safety, and most importantly… there was no apparent benefit to the product.

Find out more about lean startup methodology on our blog

How to create a mobile app using lean startup methodology

The key benefit of validating your lean startup idea is to make sure that the idea you have is a viable one before you start using resources to build and promote it. 

There are other less obvious benefits too:

• It can help you fine-tune your idea. So, it may be the case that you wanted your idea to go in a particular direction, but user research shows that pivoting may be the best thing to do
• It can help you get funding. Investors may be more likely to invest in your startup idea if you have evidence that your idea is a viable one

The value hypothesis and the growth hypothesis – are two ways to validate your idea

“To grow a successful business, validate your idea with customers” – Chad Boyda

In Eric Rie’s book ‘ The Lean Startup’ , he identifies two different types of hypotheses that entrepreneurs can use to validate their startup idea – the growth hypothesis and the value hypothesis. 

Let’s look at the two different ideas, how they compare, and how you can use them to see if your startup idea could work.

The value hypothesis tests whether your product or service provides customers with enough value and most importantly, whether they are prepared to pay for this value.

For example, let’s say that you want to develop a mobile app to help dog owners find people to help walk their dogs while they are at work. Before you start spending serious time and money developing the app, you’ll want to see if it is something of interest to your target audience. 

Your value hypothesis could say, “we believe that 60% of dog owners aged between 30 and 40 would be willing to pay upwards of €10 a month for this service.”

You then find dog owners in this age range and ask them the question. You’re pleased to see that 75% say that they would be willing to pay this amount! Your hypothesis has worked! This means that you should focus your app and your advertising on this target audience. 

If the data comes back and says your prospective target audience isn’t willing to pay, then it means you have to rethink and reframe your app before running another hypothesis. For example, you may want to focus on another demographic, or look at reducing the price of the subscription.

Shoe retailer Zappos used a value hypothesis when starting out. Founder Nick Swinmurn went to local shoe stores, taking photos of the shoes and posting them on the Zappos website. Then, if customers bought the shoes, he’d buy them from the store and send them out to them. This allowed him to see if there was interest in his website, without having to spend lots of money on stock.

The growth hypothesis tests how your customers will find your product or service and shows how your potential product could grow over the years.

Let’s go back to the dog-walking app we talked about earlier. You think that 80% of app downloads will come from word-of-mouth recommendations.

You create a minimal viable product ( MVP for short ) – this is a basic version of your app that may not contain all of the features just yet. So, you then upload it to the app stores and wait for people to start downloading it. When you have a baseline of customers, you send them an email asking them how they heard of your app.

When the feedback comes back, it shows that only 30% of downloads have come from word-of-mouth recommendations. This means that your growth hypothesis has not been successful in this scenario. 

Does this mean that your idea is a bad one? Not necessarily. It just means that you may have to look at other ways of promoting your app. If you are relying on word-of-mouth recommendations to advertise it, then it could potentially fail.

Dropbox used growth hypotheses to its advantage when creating its software. The file-storage company constantly tweaked its website, running A/B tests to see which features and changes were most popular with customers, using them in the final product.

Like any good science experiment, there are things that you need to bear in mind when running your hypotheses. Here are our recommendations:

• You may be wondering which type of hypothesis you should carry out first – a growth hypothesis or a value hypothesis. Eric Ries recommends carrying out a value hypothesis first, as it makes sense to see if there is interest before seeing how many people are interested. However, the precise order may depend on the type of product or service you want to sell;
• You will probably need to run multiple hypotheses to validate your product or service. If you do this, be sure to only test one hypothesis at a time. If you end up testing multiple ones in one go, you may not be sure which hypothesis has had which result;
• Test your most critical assumption first – this is one that you are most worried about, and could affect your idea the most. It may be that solving this issue makes your product or service a viable one;
• Specific – is your hypothesis simple? If it’s jumbled or confusing, you’re not going to get the best results from it. If you’re struggling to put together a clear hypothesis, it’s probably a sign to go back to the drawing board.
• Measurable – can your hypothesis be measured? You’ll want to get tangible results so you can check if the changes you have made have worked.
• Achievable – is your hypothesis attainable? If not, you may want to break it down into smaller goals.
• Relevant – will your hypothesis prove the validity of your product or service? 
• Timely – can your hypothesis be measured in a set amount of time? You don’t want a goal that will take years to monitor and measure!
• Be as critical as possible. If you have created an idea, it is only natural that you want it to succeed. However, being objective rather than subjective will help your startup most in the long term;
• When you are carrying out customer research, use as vast a pool of people as time and money will allow. This will result in more accurate data. The great news is that you can use social media and other networking sites to reach out to potential customers and ask them their opinions;
• When carrying out customer research, be sure to ask the questions that matter. Bear in mind that liking your product or service isn’t the same as buying it. If a customer is enthusiastic about your idea, be sure to ask follow-on questions about why they like it, or if they would be willing to spend money on it. Otherwise, your data may end up being useless;
• While it is essential to have as many relevant hypotheses as possible, be careful not to have too many.  While it may sound like a good idea to try out lots of different ideas, it can actually be counter-productive. As Eric Ries said:

“Don’t bog new teams down with too much information about falsifiable hypotheses. Because if we load our teams up with too much theory, they can easily get stuck in analysis paralysis. I’ve worked with teams that have come up with hundreds of leap-of-faith assumptions. They listed so many assumptions that were so detailed and complicated that they couldn’t decide what to do next. They were paralyzed by the just sheer quantity of the list.”

“We must learn what customers really want, not what they say they want or what we think they should want.” – Eric Ries

According to CB Insights , the number one reason why startups fail is that there is no demand for the product. Many entrepreneurs have gone ahead and launched a product that they think people want, only to find that there is no market at all.

Lean Startup Validation is essential in helping your business idea to succeed. While it may seem like extra work, the additional work you do in the beginning will be of a critical advantage later down the line.

Still not 100% convinced? Take HubSpot . Before HubSpot launched its sales and marketing services, it started off as a blog. Co-founders Dharmesh Shah and Brian Halligan used this blog to validate their ideas and see what their visitors wanted. This helped them confirm that their concept was on the right lines and meant they could launch a product that people actually wanted to use.

Validating a startup idea before development is crucial because it ensures that the idea is viable and addresses a real problem that customers have. With a high failure rate of new products, validation helps avoid wasting time and resources on ideas that might not succeed.

The value hypothesis tests whether customers find enough value in a product or service to pay for it. The growth hypothesis examines how customers will discover and adopt the product over time. Both hypotheses are essential for validating the viability of a startup idea.

Eric Ries recommends starting with a value hypothesis before a growth hypothesis. Validating whether the idea provides value is crucial before considering how to promote and grow it.

When creating and running a hypothesis, consider the following: 1. Focus on testing one hypothesis at a time. 2. Test your most critical assumptions first. 3. Ensure your hypothesis follows SMART goals (Specific, Measurable, Achievable, Relevant, Timely). 4. Use a wide pool of potential customers for accurate data. 5. Ask relevant and probing questions during customer research. 6. Avoid overwhelming your team with excessive hypotheses.

Validating your product idea before development helps you avoid the top reason for startup failure—lack of demand for the product. By confirming that there is a market need and interest in your idea, you increase the chances of building a successful product.

Lean Startup Validation helps entrepreneurs avoid the mistake of launching a product that doesn’t address a genuine need. By gathering evidence and feedback early, you can make informed decisions about pivoting or refining your idea before investing significant time and resources.

Certainly. Suppose you’re developing a mobile app for dog owners to find dog-walking services. Your value hypothesis could be: “We believe that 60% of dog owners aged between 30 and 40 would be willing to pay upwards of €10 a month for this service.” You then validate this hypothesis by surveying dog owners in that age range and analyzing their responses.

The growth hypothesis examines how customers will discover and adopt your product. If, for example, you expect 80% of app downloads to come from word-of-mouth recommendations, but feedback shows only 30% are from this source, you may need to reevaluate your promotion strategy.

Yes, Lean Startup Validation can be applied to startups across various industries. Whether you’re offering a product or service, the process of testing hypotheses and gathering evidence applies universally to ensure the viability of your idea.

To gather accurate data, focus on reaching a diverse pool of potential customers through various channels, including social media and networking sites. Ask relevant questions about their preferences, willingness to pay, and potential pain points related to your idea

Being critical and objective during validation helps you avoid confirmation bias and wishful thinking. Objectivity allows you to assess whether your idea truly addresses a problem and resonates with customers, ensuring that your startup’s foundation is built on solid evidence.

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12 min read

Value Hypothesis 101: A Product Manager's Guide

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Humans make assumptions every day—it’s our brain’s way of making sense of the world around us, but assumptions are only valuable if they're verifiable . That’s where a value hypothesis comes in as your starting point.

A good hypothesis goes a step beyond an assumption. It’s a verifiable and validated guess based on the value your product brings to your real-life customers. When you verify your hypothesis, you confirm that the product has real-world value, thus you have a higher chance of product success. 

What Is a Verifiable Value Hypothesis?

A value hypothesis is an educated guess about the value proposition of your product. When you verify your hypothesis , you're using evidence to prove that your assumption is correct. A hypothesis is verifiable if it does not prove false through experimentation or is shown to have rational justification through data, experiments, observation, or tests. 

The most significant benefit of verifying a hypothesis is that it helps you avoid product failure and helps you build your product to your customers’ (and potential customers’) needs. 

Verifying your assumptions is all about collecting data. Without data obtained through experiments, observations, or tests, your hypothesis is unverifiable, and you can’t be sure there will be a market need for your product. 

A Verifiable Value Hypothesis Minimizes Risk and Saves Money

When you verify your hypothesis, you’re less likely to release a product that doesn’t meet customer expectations—a waste of your company’s resources. Harvard Business School explains that verifying a business hypothesis “...allows an organization to verify its analysis is correct before committing resources to implement a broader strategy.” 

If you verify your hypothesis upfront, you’ll lower risk and have time to work out product issues. 

UserVoice Validation makes product validation accessible to everyone. Consider using its research feature to speed up your hypothesis verification process. 

Value Hypotheses vs. Growth Hypotheses 

Your value hypothesis focuses on the value of your product to customers. This type of hypothesis can apply to a product or company and is a building block of product-market fit . 

A growth hypothesis is a guess at how your business idea may develop in the long term based on how potential customers may find your product. It’s meant for estimating business model growth rather than individual products. 

Because your value hypothesis is really the foundation for your growth hypothesis, you should focus on value hypothesis tests first and complete growth hypothesis tests to estimate business growth as a whole once you have a viable product.

4 Tips to Create and Test a Verifiable Value Hypothesis

A verifiable hypothesis needs to be based on a logical structure, customer feedback data , and objective safeguards like creating a minimum viable product. Validating your value significantly reduces risk . You can prevent wasting money, time, and resources by verifying your hypothesis in early-stage development. 

A good value hypothesis utilizes a framework (like the template below), data, and checks/balances to avoid bias. 

1. Use a Template to Structure Your Value Hypothesis 

By using a template structure, you can create an educated guess that includes the most important elements of a hypothesis—the who, what, where, when, and why. If you don’t structure your hypothesis correctly, you may only end up with a flimsy or leap-of-faith assumption that you can’t verify. 

A true hypothesis uses a few guesses about your product and organizes them so that you can verify or falsify your assumptions. Using a template to structure your hypothesis can ensure that you’re not missing the specifics.

You can’t just throw a hypothesis together and think it will answer the question of whether your product is valuable or not. If you do, you could end up with faulty data informed by bias , a skewed significance level from polling the wrong people, or only a vague idea of what your customer would actually pay for your product. 

A template will help keep your hypothesis on track by standardizing the structure of the hypothesis so that each new hypothesis always includes the specifics of your client personas, the cost of your product, and client or customer pain points. 

A value hypothesis template might look like: 

[Client] will spend [cost] to purchase and use our [title of product/service] to solve their [specific problem] OR help them overcome [specific obstacle]. 

An example of your hypothesis might look like: 

B2B startups will spend $500/mo to purchase our resource planning software to solve resource over-allocation and employee burnout.

By organizing your ideas and the important elements (who, what, where, when, and why), you can come up with a hypothesis that actually answers the question of whether your product is useful and valuable to your ideal customer. 

2. Turn Customer Feedback into Data to Support Your Hypothesis  

Once you have your hypothesis, it’s time to figure out whether it’s true—or, more accurately, prove that it’s valid. Since a hypothesis is never considered “100% proven,” it’s referred to as either valid or invalid based on the information you discover in your experiments or tests. Additionally, your results could lead to an alternative hypothesis, which is helpful in refining your core idea.

To support value hypothesis testing, you need data. To do that, you'll want to collect customer feedback . A customer feedback management tool can also make it easier for your team to access the feedback and create strategies to implement or improve customer concerns. 

If you find that potential clients are not expressing pain points that could be solved with your product or you’re not seeing an interest in the features you hope to add, you can adjust your hypothesis and absorb a lower risk. Because you didn’t invest a lot of time and money into creating the product yet, you should have more resources to put toward the product once you work out the kinks. 

On the other hand, if you find that customers are requesting features your product offers or pain points your product could solve, then you can move forward with product development, confident that your future customers will value (and spend money on) the product you’re creating. 

A customer feedback management tool like UserVoice can empower you to challenge assumptions from your colleagues (often based on anecdotal information) which find their way into team decision making . Having data to reevaluate an assumption helps with prioritization, and it confirms that you’re focusing on the right things as an organization.

3. Validate Your Product 

Since you have a clear idea of who your ideal customer is at this point and have verified their need for your product, it’s time to validate your product and decide if it’s better than your competitors’. 

At this point, simply asking your customers if they would buy your product (or spend more on your product) instead of a competitor’s isn’t enough confirmation that you should move forward, and customers may be biased or reluctant to provide critical feedback. 

Instead, create a minimum viable product (MVP). An MVP is a working, bare-bones version of the product that you can test out without risking your whole budget. Hypothesis testing with an MVP simulates the product experience for customers and, based on their actions and usage, validates that the full product will generate revenue and be successful.  

If you take the steps to first verify and then validate your hypothesis using data, your product is more likely to do well. Your focus will be on the aspect that matters most—whether your customer actually wants and would invest money in purchasing the product.

4. Use Safeguards to Remain Objective 

One of the pitfalls of believing in your product and attempting to validate it is that you’re subject to confirmation bias . Because you want your product to succeed, you may pay more attention to the answers in the collected data that affirm the value of your product and gloss over the information that may lead you to conclude that your hypothesis is actually false. Confirmation bias could easily cloud your vision or skew your metrics without you even realizing it. 

Since it’s hard to know when you’re engaging in confirmation bias, it’s good to have safeguards in place to keep you in check and aligned with the purpose of objectively evaluating your value hypothesis. 

Safeguards include sharing your findings with third-party experts or simply putting yourself in the customer’s shoes.

Third-party experts are the business version of seeking a peer review. External parties don’t stand to benefit from the outcome of your verification and validation process, so your work is verified and validated objectively. You gain the benefit of knowing whether your hypothesis is valid in the eyes of the people who aren’t stakeholders without the risk of confirmation bias. 

In addition to seeking out objective minds, look into potential counter-arguments , such as customer objections (explicit or imagined). What might your customer think about investing the time to learn how to use your product? Will they think the value is commensurate with the monetary cost of the product? 

When running an experiment on validating your hypothesis, it’s important not to elevate the importance of your beliefs over the objective data you collect. While it can be exciting to push for the validity of your idea, it can lead to false assumptions and the permission of weak evidence. 

Validation Is the Key to Product Success

With your new value hypothesis in hand, you can confidently move forward, knowing that there’s a true need, desire, and market for your product.

Because you’ve verified and validated your guesses, there’s less of a chance that you’re wrong about the value of your product, and there are fewer financial and resource risks for your company. With this strong foundation and the new information you’ve uncovered about your customers, you can add even more value to your product or use it to make more products that fit the market and user needs. 

If you think customer feedback management software would be useful in your hypothesis validation process, consider opting into our free trial to see how UserVoice can help.

Heather Tipton

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Tips to Create and Test a Value Hypothesis: A Step-by-Step Guide

Developing a robust value hypothesis is crucial as you bring a new product to market, guiding your startup toward answering a genuine market need. Constructing a verifiable value hypothesis anchors your product's development process in customer feedback and data-driven insight rather than assumptions.

This framework enables you to clarify the potential value your product offers and provides a foundation for testing and refining your approach, significantly reducing the risk of misalignment with your target market. To set the stage for success, employ logical structures and objective measures, such as creating a minimum viable product, to effectively validate your product's value proposition.

What Is a Verifiable Value Hypothesis?

A verifiable value hypothesis articulates your belief about how your product will deliver value to customers. It is a testable prediction aimed at demonstrating the expected outcomes for your target market.

To ensure that your value hypothesis is verifiable, it should adhere to the following conditions:

• Specific : Clearly defines the value proposition and the customer segment.
• Measurable : Includes metrics by which you can assess success or failure.
• Achievable : Realistic based on your resources and market conditions.
• Relevant : Directly addresses a significant customer need or desire.
• Time-Bound : Has a defined period for testing and validation.

When you create a value hypothesis, you're essentially forming the backbone of your business model. It goes beyond a mere assumption and relies on customer feedback data to inform its development. You also safeguard it with objective measures, such as a minimum viable product, to test the hypothesis in real life.

By articulating and examining a verifiable value hypothesis, you understand your product's potential impact and reduce the risk associated with new product development. It's about making informed decisions that increase your confidence in the product's potential success before committing significant resources.

Value Hypotheses vs. Growth Hypotheses

Value hypotheses and growth hypotheses are two distinct concepts often used in business, especially in the context of startups and product development.

Value Hypotheses : A value hypothesis is centered around the product itself. It focuses on whether the product truly delivers customer value. Key questions include whether the product meets a real need, how it compares to alternatives, and if customers are willing to pay for it. Valuing a value hypothesis is crucial before a business scales its operations.

Growth Hypotheses : A growth hypothesis, on the other hand, deals with the scalability and marketing aspects of the business. It involves strategies and channels used to acquire new customers. The focus is on how to grow the customer base, the cost-effectiveness of growth strategies, and the sustainability of growth. Validating a growth hypothesis is typically the next step after confirming that the product has value to the customers.

In practice, both hypotheses are crucial for the success of a business. A value hypothesis ensures the product is desirable and needed, while a growth hypothesis ensures that the product can reach a larger market effectively.

Tips to Create and Test a Verifiable Value Hypothesis

Creating a value hypothesis is crucial for understanding what drives customer interest in your product. It's an educated guess that requires rigor to define and clarity to test. When developing a value hypothesis, you're attempting to validate assumptions about your product's value to customers. Here are concise tips to help you with this process:

1. Understanding Your Market and Customers

Before formulating a hypothesis, you need a deep understanding of your market and potential customers. You're looking to uncover their pain points and needs which your product aims to address.

Begin with thorough market research and collect customer feedback to ensure your idea is built upon a solid foundation of real-world insights. This understanding is pivotal as it sets the tone for a relevant and testable hypothesis.

• Define Your Value Proposition Clearly: Articulate your product's value to the user. What problem does it solve? How does it improve the user's life or work?
• Identify Your Target Audience. Determine who your ideal customers are. Understand their needs, pain points, and how they currently address the problem your product intends to solve.

2. Defining Clear Assumptions

The next step is to outline clear assumptions based on your idea that you believe will bring value to your customers. Each assumption should be an assertion that directly relates to how your customers will find your product valuable.

For example, if your product is a task management app, you might assume that the ability to share task lists with team members is a pain point for your potential customers. Remember, assumptions are not facts—they are educated guesses that need verification.

3. Identify Key Metrics for Your Hypothesis Test

Once you've defined your assumptions, delineate the framework for testing your value hypothesis. This involves designing experiments that validate or invalidate your assumptions with measurable outcomes. Ensure that your hypothesis can be tested with measurable outcomes. This could be in the form of user engagement metrics, conversion rates, or customer satisfaction scores.

Determine what success looks like and define objective metrics that will prove your product's value. This could be user engagement, conversion rates, or revenue. Choosing the right metrics is essential for an accurate test. For instance, in your test, you might measure the increase in customer retention or the decrease in time spent on task organization with your app. Construct your test so that the results are unequivocal and actionable.

4. Construct a Testable Proposition

Formulate your hypothesis in a way that can be tested empirically. Use qualitative research methods such as interviews, surveys, and observation to gather data about your potential users. Formulate your value hypothesis based on insights from this research. Plan experiments that can validate or invalidate your value hypothesis. This might involve A/B testing, user testing sessions, or pilot programs.

A good example is to posit that "Introducing feature X will increase user onboarding by Y%." Avoid complexity by testing one variable simultaneously. This helps you identify which changes are actually making a difference.

5. Applying Evidence to Innovation

When your data indicates a promising avenue for product development , it's imperative that you validate your growth hypothesis through experimentation. Align your value proposition with the evidence at hand.

Develop a simplified version of your product that allows you to test the core value proposition with real users without investing in full-scale production. Start by crafting a minimum viable product ( MVP ) to begin testing in the market. This approach helps mitigate risk by not investing heavily in unproven ideas. Use analytics tools to collect data on how users interact with your MVP. Look for patterns that either support or contradict your value hypothesis.

If the data suggests that your value hypothesis is wrong, be prepared to revise your hypothesis or pivot your product strategy accordingly.

6. Gather Customer Feedback

Integrating customer feedback into your product development process can create a more tailored value proposition. This step is crucial in refining your product to meet user needs and validate your hypotheses.

Use customer feedback tools to collect data on how users interact with your MVP. Look for patterns that either support or contradict your value hypothesis. Here are some ways to collect feedback effectively :

• Feedback portals
• User testing sessions
• In-app feedback
• Website widgets
• Direct interviews
• Focus groups
• Feedback forums

Create a centralized place for product feedback to keep track of different types of customer feedback and improve SaaS products while listening to their customers. Rapidr helps companies be more customer-centric by consolidating feedback across different apps, prioritizing requests, having a discourse with customers, and closing the feedback loop.

7. Analyze and Iterate Quickly

Review the data and analyze customer feedback to see if it supports your hypothesis. If your hypothesis is not supported, iterate on your assumptions, and test again. Keep a detailed record of your hypotheses, experiments, and findings. This documentation will help you understand the evolution of your product and guide future decision-making.

Use the feedback and data from your tests to make quick iterations of your product and drive product development . This allows you to refine your value proposition and improve the fit with your target audience. Engage with your users throughout the process. Real-world feedback is invaluable and can provide insights that data alone cannot.

• Identify Patterns : What commonalities are present in the feedback?
• Implement Changes : Prioritize and make adjustments based on customer insights.

9. Align with Business Goals and Stay Customer-Focused

Ensure that your value hypothesis aligns with the broader goals of your business. The value provided should ultimately contribute to the success of the company. Remember that the ultimate goal of your value hypothesis is to deliver something that customers find valuable. Maintain a strong focus on customer needs and satisfaction throughout the process.

10. Communicate with Stakeholders and Update them

Keep all stakeholders informed about your findings and the implications for the product. Clear communication helps ensure everyone is aligned and understands the rationale behind product decisions. Communicate and close the feedback loop with the help of a product changelog through which you can ​​announce new changes and engage with customers.

Understanding and validating a value hypothesis is essential for any business, particularly startups. It involves deeply exploring whether a product or service meets customer needs and offers real value. This process ensures that resources are invested in desirable and useful products, and it's a critical step before considering scalability and growth.

By focusing on the value hypothesis, businesses can better align their offerings with market demand, leading to more sustainable success. Placing customer feedback at the center of the process of testing a value hypothesis helps you develop a product that meets your customers' needs and stands out in the market.

Rapidr helps companies be more customer-centric by consolidating feedback across different apps, prioritizing requests, having a discourse with customers, and closing the feedback loop.

Build better products with user feedback

Rapidr helps SaaS companies understand what customers need through feedback, prioritize what to build next, inform the roadmap, and notify customers on product releases

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Every time you propose a partnership with another company, you are making a guess as to why they'd want to work with you.  According to the Grand Unified Theory of Business Development , business development is fundamentally focused on creating long term value.  But whose value are you taking into consideration?

When two companies are evaluating an opportunity to work together, each is weighing the relative value of the same deal from their own unique perspective.  While you approach a company with an understanding of the value that they can bring to you and your organization, they are making the same judgement what you can bring to them - and will decide accordingly on how to proceed.

Properly preparing to engage a prospective partner requires you to make an assumption about what would be a satisfactory response to the most pertinent question on their minds: “What’s in it for me?”  I call this your Value Hypothesis .

What Is a Value Hypothesis

As any 7th grade grade science student can tell you, a hypothesis is an assumption that can be validated through experimentation and observation.  Similarly, a Value Hypothesis should be a testable statement that can be validated or refuted when facing your prospective partner.

Take an example:

“ Widgetco’s products sold through Gearly Inc.’s well-established sales channels in Europe can drive $15MM in incremental sales annually ,” might read one conceived by Gearly in an effort to court Widgetco in partnership.

Whether written merely to strategize how to connect with a potential partner or put directly into the context of an introductory email, your Value Hypothesis will inform your approach to engaging in partnership discussions from the very start.  As written from the perspective of one company, a Value Hypothesis makes an educated guess about whether and why an opportunity has any true appeal for the other.

Formulating a Value Hypothesis

A well-understood and carefully crafted Value Hypothesis enables a more deeply engaging partnership discussion at every stage from first contact to getting ink on a contract.

The more you can do to forecast the other side’s reaction to your Value Hypothesis, the more you can do to affect their response to it.  The more appealing you can make the prospect of partnering with you, the more likely you are to get a meeting, to get a deal, and to get a successful partnership in place that drives mutual value for the long-term.

There are a number of considerations to include when formulating your Value Hypothesis:

• Can you clearly state the potential value of this opportunity to them?
• Does this opportunity mesh with the organization’s strategy?  If not, might they consider it a worthwhile shift in their priorities?
• Is this opportunity significant enough to be considered worth their time and energy?
• Are there reasons why partnering with you would be the best route for them to realize the value of this opportunity?
• Have they publicly signaled (in the press, a 10-K, other partnerships, or in their actions) an interest in the type of opportunity that you're proposing?

Validating the Value Hypothesis

Formulating a Value Hypothesis forces you to put a stake in the ground before approaching a partner, but when put in front of the partner those assumptions will be subjected to an exhaustive battery of tests that may determine the fate of your deal.

Validation #1 - Getting a Meeting

Everyone’s busy - especially those folks who you believe would make for great partners (guess what: lots of other people probably think they’d be great partners too).  So why would someone take the time to respond to your request to have a meeting, let alone afford you a precious time slot on their highly curated calendar?

Putting forth the effort to conceive, challenge, and refine your vision of what value your partner can realize by meeting with you will go a long way in securing that first sit-down.

Your initial outreach, be it an email after an introduction or a cold call to someone you’ve never met, needs to offer your counterpart a compelling reason to take the time to meet with you.  This doesn’t only go for warm leads that come with an introduction - as warm and toasty as your first contact may be, any introduction that’s not followed by a compelling reason to meet with you suffers a lower chance of earning a response.

The degree to which someone may accept or decline your invitation to engage in further discussions depends heavily on a number of variables.  Take our preceding example of a Value Hypothesis used by Gearly when approaching Widgetco:

Widgetco’s products sold through Gearly’s well-established sales channels in Europe can drive $15MM in incremental sales annually.

Perhaps Widgetco was motivated by the Gearly’s original intent of helping them enter the European market, and would take a meeting to explore that opportunity.

But perhaps instead they are interested in pursuing a similar distribution partnership across Asia, Africa, and the Middle East.  Although those markets may not have been explicitly stated in the original outreach sent by Gearly, the very prospect that an opportunity to enter those markets may enter into the discussions could suffice enough to encourage Widgetco to take the initial meeting based on Gearly’s proposal.

A well-reasoned Value Hypothesis may incite a large enough spark of excitement to get you in the door, but keeping up that momentum requires you to continue your effort to understand and validate “what’s in it for them.”

Validation #2 - Building Interest

Getting through the gate and into a partner meeting is a feat that implies some degree of accuracy in your original Value Hypothesis.  And yet, there is still much work to be done to ensure that the opportunity to forge a deal remain alive throughout the remainder of the partnership discussions.  As you embark down the path of fleshing out the form and structure of a collaboration, the questions around whether it’s worth it for these talks to proceed to a signed deal will only get more intense.

Take again our preceding Value Hypothesis example:

What’s the best way to validate your Value Hypothesis once you’re in the door?  Just ask:

“ We at Gearly would propose a distribution partnership by which Widgetco sells your products into the European market via our sales channels.  How does that sound to you? ”

How might Widgetco react to the suggestion of this opportunity?  Does Widgetco have any interest in  entering into the European market, or might they envision a move into other geographic markets as a higher priority?  Is a $15MM opportunity enough to whet their appetite, or might they require a more expansive partnership to make the effort worthwhile with at least $50MM in potential.  Or yet still might Widgetco find the option of international expansion appealing, but are less than sure that working with Gearly is the best path to pursuing the opportunity?

Starting with a well-informed Value Hypothesis on how cooperating can realize long-term value for both companies provides an anchor point for a partnership discussion that can evolve organically.  And now you have the opportunity to work collaboratively with your partner to determine if there is a path forward that creates enough long-term value for both sides to warrant a deal.

Validation #3 - Closing a Deal

Almost incontrovertibly, the initial outline of a deal that you proposed will be subject to the ideas, edits, and whims of your partner.  Incorporating the input and feedback of your prospective partner is crucial to securing their engagement on a partnership, but so too is it crucial to make sure the newly-refined opportunity at hand still creates enough long-term value for your organization to be satisfied in pursuing it.

As you proceed through the partnership discussions and further define the shape of the partnership, the need to validate whether the the deal makes sense flips back on to you: as the structure of a partnership morphs throughout the negotiation, is the opportunity that’s on the table still one that you find valuable enough to pursue?

Let’s take one final look at our Value Hypothesis example - now modified with potential revisions embedded during the course of the discussions:

Widgetco’s products sold through Gearly’s well-established sales channels in Asia, Africa, and the Middle East can drive $50MM in incremental sales annually.

Does Widgetco’s edits to the selected markets change Gearly’s interest in the deal?  Do they have the resources, capacity, and interest to pursue the opportunity now that it looks different from what was originally intended?  Is this still an opportunity that Gearly finds worth pursuing?

In effect, before proceeding to the closing of a deal, now you must compare the potential for long-term value that can be created from the opportunity as it now stands against the your own organization’s wants and needs.  After the impact of the revisions of the negotiation process, does the Value Hypothesis of the deal that’s been structured still work for you?

What’s In It For Us?

Leaping the hurdles required to bring a partnership to market can require a quick jog or a slow slog, but the process of vetting and validating the prospective deal starts and ends with the same question.  To get past the starting line, one must develop a perspective on “what’s in it for them.”  To make it to the finish, any partnership must create a balanced answer to “what’s in it for us.”

Scott Pollack writes, lectures, and teaches about business development.  The above article is an excerpt from his upcoming book, The Start of the Deal .   Follow him on Twitter as  @slpollack and at  http://www.startofthedeal.com

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Value Hypothesis 101: Everything you need to know

Product managers devote a lot of time and resources to developing features. But how can we guarantee that such features address client issues? How can we ensure that they convert into a successful product?

Here’s the sad truth: 90% of all startup companies fail . This happens primarily because there is a mismatch between what a product provides and the market’s requirements. This disconnect might cause product failure.

The good news is that you have a great solution to close this gap: the Value Hypothesis. This hypothesis is a framework that enables developers to test if the features they’re building are truly valuable to their target users.

By validating these assumptions early and often, developers can avoid wasting time and resources on features that customers don’t want or need. This focus on value ensures that the final product resonates with users and solves their problems effectively.

This guide will equip you with a step-by-step approach to crafting a strong value hypothesis. We’ll then delve into how you can leverage this hypothesis to design solutions that delight your customers, ensuring their satisfaction and enjoyment.

Fundamentals of Value Hypothesis

Before we get started with the value hypothesis guide, let’s look at the fundamentals of the Value Hypothesis:

Definition and Scope of Value Hypothesis

A value hypothesis is an informed assumption regarding your product’s value proposition. It is a testable statement. It anticipates how a product will solve a particular problem for a given consumer group. 

But the value hypothesis doesn’t stop at launch. It serves as a compass throughout development, helping developers understand what truly matters to their target audience. This insight allows them to prioritize features that resonate with users, ultimately leading to a product that delights your customers.

Critical Components of Value Hypothesis

A robust value hypothesis is built on three key components:

1. Value Proposition

The value proposition describes the main advantage that your product provides to clients. It’s the “what” and “why” of your product. Your product’s Value Proposition describes how it alleviates a pain point. This solution is tailored to a particular consumer segment.

2. Customer Segmentation

Identifying your target consumer persona is critical. This includes studying their demographics, requirements, behaviors, and pain spots. By segmenting your target market, you may adjust your value proposition to appeal to a specific demographic.

3. Problem Statement

Clearly explain the problem your product is intended to tackle. This should be a specific, actionable pain problem your target client segment faces.

Importance of Value Hypothesis in Product Management

The value hypothesis acts as a north star for product development and management. Here’s how the Value Hypothesis is helpful: 

Focusing on client requirements prioritizes features that address actual problems and provide value.

Validating your hypothesis early reduces wasted resources on features that no one wants.

A defined value hypothesis improves communication among product teams, designers, and stakeholders.

Crafting a Strong Value Hypothesis

Now that you understand the fundamentals, let’s craft a compelling value hypothesis. Here’s a two-pronged approach supported by readily available value hypothesis templates to guide your process:

1. Research and Analysis

The first step is to conduct thorough market research. Understand existing solutions, competitor offerings, and industry trends through market research. This helps you differentiate your product and identify unmet customer needs.

Next, leverage user interviews, surveys, and customer support data to glean insights into your target audience’s pain points and desired outcomes. 

2. Identifying Customer Needs

After you’ve completed your research and analysis, we need to determine client needs. By integrating market research and user input, you may determine consumer requirements. Here are some questions to help assist you:

What are the most common difficulties and obstacles encountered by your target client segment?

What existing options are available, and what are their limitations?

What are your target audience’s unfulfilled requirements or desires?

Validating the Value Hypothesis

Once you’ve crafted a value hypothesis using a value hypothesis template, the next step is to test its validity. Here’s how:

MVP Testing

Develop a minimum viable product (MVP) – a bare-bones version of your product with core functionalities. This allows you to test your value proposition with real users and gather feedback at a minimal cost.

Prototyping

Create prototypes to visualize the product concept. Gather user feedback on the overall user experience and value proposition.

Metrics for Evaluation

Once you get all the data related to your hypothesis, it’s time to analyze these data points. Here are some metrics you can use to test your Value Hypothesis:

User Engagement

Track metrics like time spent on the platform, feature usage, and repeat visits to gauge user engagement with your MVP or prototype.

Conversion Rates

Measure conversion rates for key actions like signups, purchases, or feature adoption. Evaluating these conversion rates lets you understand whether your value proposition resonates with users.

Iterative Improvement of Value Hypothesis

The beauty of the value hypothesis framework lies in its iterative nature. Here’s how to refine your hypothesis:

Establish a feedback loop to collect user data during product development. 

Analyze user feedback to identify areas for improvement. Iterate on your value proposition based on user insights.

Adaptation to Market Changes

The market is dynamic, so your value hypothesis must also adapt. Stay updated on industry trends and track user behavior shifts. Adjust your value proposition to stay relevant and competitive. 

Here are some strategies to ensure your value hypothesis remains dynamic:

Conduct regular market research to stay abreast of industry trends and competitor innovations.

Continuously monitor user feedback and identify emerging pain points or unmet needs.

Conduct A/B tests for different value propositions and features to see what resonates best with your target audience.

Common Pitfalls to Avoid

While the value hypothesis framework offers a powerful approach, there are pitfalls to be aware of:

Avoid confirmation bias: It’s easy to focus on facts that confirm your initial premise. However, it is critical to consider contradictory feedback. Actively seek out alternative opinions and make sure your hypothesis is customer-centric.

Beware of “shiny object syndrome”: Only be influenced by the latest technological advances if they meet a fundamental client requirement. Your value offer should be based on solving real-world challenges for your target audience.

Do not become attached to your original hypothesis: Your value offer should evolve along with the market. Prepare to modify your hypothesis depending on the evidence and user input.

Don’t mix activity and progress: While gathering user input is crucial, it’s the analysis that unlocks true value. By diving deep into the data, you’ll uncover actionable insights that inform adjustments to your product.

Value hypothesis: action points

Building a successful solution or product demands a thorough grasp of your target consumer and the value you provide. The value hypothesis framework provides you with an effective instrument to do this.

You can create a convincing value proposition by following the stages in this value hypothesis guide. Validate its efficacy and iterate often to keep your product current and helpful to your consumers.

A solid value hypothesis is a live document that adapts to your product and market.  By taking a data-driven, customer-centric strategy, you’ll be well on your way to creating a successful solution.

Are you ready to implement the value hypothesis framework? Explore value hypothesis 101 resources and templates to craft a winning product strategy. Consider using featureOS as a customer feedback management solution. This program simplifies the process of gathering, assessing, and incorporating user input. Contact us to learn more today!

Karthik Kamalakannan

Founder and CEO

Published on

Sat May 11 2024 11:06:08 GMT+0000 (Coordinated Universal Time)

Time to value: 6 min read

Close feedback loop, the right way

featureOS is a feedback aggregation and analysis tool from various sources for product teams.

Understanding Lean Startup Validation: What Is a Value Hypothesis?

Understanding Lean Startup Principles in Relation to Value Hypothesis

When exploring the Lean Startup methodology and its relationship with the value hypothesis , it's essential to understand how these principles intertwine. By integrating lean principles with the value hypothesis , entrepreneurs can effectively validate their startup ideas and drive sustainable business development.

Lean Startup Methodology and Value Hypothesis

Integrating lean principles with value hypothesis.

Incorporating the core tenets of lean startup methodology, such as rapid iteration and validated learning, into the formulation of a value hypothesis is crucial for refining and validating a startup idea.

Lean Startup Benefits for Value Hypothesis

The lean startup approach offers invaluable benefits for shaping a robust value hypothesis, including efficient resource allocation, risk mitigation, and accelerated product-market fit.

Aligning Lean Principles with Value Hypothesis

Aligning lean principles with the value hypothesis ensures that startups remain customer-centric, adaptable, and responsive to market dynamics.

Importance of Lean Startup Validation for Value Hypothesis

Reducing business risks through value hypothesis.

Validating a value hypothesis through lean principles minimizes the inherent risks associated with untested assumptions, thereby safeguarding business resources.

Enhancing Product Market Fit with Value Hypothesis

By leveraging lean startup validation processes, entrepreneurs can enhance their product-market fit by aligning their offerings closely with customer needs and preferences.

Accelerating Business Growth with Value Hypothesis

A well-validated value hypothesis paves the way for accelerated business growth by fostering innovation, customer satisfaction, and competitive differentiation.

Lean Startup Validation process: The integration of lean principles into the validation process is instrumental in ensuring that startups develop viable solutions that resonate with their target audience.

Understanding the Value Hypothesis

In the realm of lean startup methodology , a value hypothesis plays a pivotal role in shaping the trajectory of a new venture. It serves as a foundational premise that guides entrepreneurs in developing products or services that resonate with their target audience, thereby increasing the likelihood of success.

Defining a Value Hypothesis

Components of a value hypothesis.

A value hypothesis comprises several essential components, including the identification of customer pain points, an articulation of how the proposed solution addresses these pain points, and an estimation of how customers will perceive and adopt the solution.

Importance of a Clear Value Hypothesis

Crafting a clear and concise value hypothesis is crucial for aligning internal teams, investors, and stakeholders around a common understanding of the problem being solved and the proposed solution. It provides clarity and direction for all subsequent product development efforts.

Crafting an Effective Value Hypothesis

An effective value hypothesis is not only specific but also measurable. It should articulate clear success criteria that can be objectively evaluated to determine whether the proposed solution has indeed created value for its intended users.

The Role of a Value Hypothesis in Lean Startup

Aligning with customer needs.

The value hypothesis makes explicit assumptions about what customers truly value and how those values can be addressed through innovative solutions. This alignment ensures that startups remain focused on delivering tangible benefits to their customers.

Iterative Product Development

Embracing a value hypothesis within lean startup principles fosters iterative product development, where each iteration is designed to test and validate specific aspects of the value proposition. This iterative approach allows for continuous refinement based on real-time feedback from customers.

Behind the Scenes

Historical Examples:

Eric Ries' first company, Catalyst Recruiting , failed because he and his colleagues did not understand the wants of their target customers, focusing too much time and energy on the initial product launch. Ries later published “ The Lean Startup ” book in 2011, aiming to improve upon what had been going on with startups and tech companies. He was inspired by his own company's failure due to not understanding customer needs.

Expert Testimony:

"Lean startup emphasizes customer feedback over intuition and flexibility over planning."

"Testing and validating your hypotheses is an essential part of startup development as it helps you reduce uncertainty, avoid failure, and create value for your customers."

Importance of a Value Hypothesis

In the realm of startup development, a value hypothesis serves as the compass guiding entrepreneurs toward creating products or services that resonate with their target audience. Understanding the significance of a value hypothesis entails embracing a customer-centric approach and leveraging market differentiation strategies.

Customer-Centric Approach to Value Hypothesis

Understanding customer pain points for value hypothesis.

A fundamental aspect of a value hypothesis involves delving into the pain points experienced by customers . By comprehensively understanding these pain points, entrepreneurs can tailor their solutions to directly address the specific needs and challenges faced by their target audience.

Tailoring Products to Customer Needs with Value Hypothesis

The essence of a value hypothesis lies in its ability to steer product development efforts toward catering to the unique requirements and preferences of customers . This tailored approach ensures that the resulting offerings align closely with what customers truly value, thereby increasing the likelihood of widespread adoption and satisfaction.

Building Customer Loyalty through Value Hypothesis

Statistical data highlights that at least one-third of respondents emphasize human interaction as crucial for their loyalty, while more than half express a preference for an enjoyable online shopping experience. A well-crafted value hypothesis enables entrepreneurs to build customer loyalty by addressing these key aspects that influence consumer allegiance.

Market Differentiation and Value Hypothesis

Identifying unique value propositions.

An effective value hypothesis aids in identifying and articulating unique value propositions that set a venture apart from competitors. By pinpointing what makes their offerings distinct, entrepreneurs can effectively communicate this differentiation to potential customers, fostering brand loyalty and preference.

Creating Competitive Advantage through Value Hypothesis

The strategic formulation and validation of a robust value hypothesis empower startups to create sustainable competitive advantages within their respective markets. This advantage stems from aligning products or services with customer needs in ways that outperform existing alternatives, positioning the venture for long-term success.

Validating the Value Hypothesis

In the realm of lean startup methodology, validating the value hypothesis is a critical phase that involves leveraging various methods and tools to ensure that a product or service genuinely delivers value to the customer. This process not only reduces uncertainty but also paves the way for creating solutions that address real needs and preferences.

Research and Data Analysis for Value Hypothesis

Conducting market research for value hypothesis.

Market research serves as a foundational step in validating the value hypothesis. It involves gathering insights into consumer behavior, market trends, and competitor offerings to assess the potential reception of the proposed solution.

Analyzing User Feedback for Value Hypothesis

User feedback analysis provides invaluable qualitative data regarding how customers perceive and interact with a product or service. This analysis helps in refining the value hypothesis based on authentic user experiences and preferences.

Iterative Testing of Value Hypothesis

Prototyping and mvp testing for value hypothesis.

Prototyping and minimum viable product (MVP) testing are instrumental in validating the value hypothesis. These methods allow entrepreneurs to gather real-world feedback on early versions of their offerings, enabling iterative refinement based on user responses.

A/B Testing and Experiments for Value Hypothesis

A/B testing involves comparing different versions of a product or feature to determine which resonates best with users. By conducting experiments through A/B testing, startups can validate their value hypotheses by identifying features that drive meaningful engagement and satisfaction.

Measuring Customer Value with Value Hypothesis

Key metrics for value assessment with value hypothesis.

Key metrics, such as customer acquisition cost, lifetime value, and retention rates, provide quantifiable indicators of customer value. Measuring these metrics allows startups to gauge how well their offerings align with customer needs and expectations.

Customer Satisfaction Surveys for Value Hypothesis

Customer satisfaction surveys offer direct insights into how customers perceive the value delivered by a product or service. These surveys help in understanding areas of strength and improvement within the value proposition.

Long-Term Value Measurement with Value Hypothesis

Long-term measurement involves tracking customer satisfaction, loyalty, and advocacy over extended periods. This longitudinal assessment provides a comprehensive view of how well a product or service continues to deliver value over time.

By employing these validation methods , startups can systematically refine their value hypotheses based on empirical evidence gathered from market dynamics and user interactions.

Key Recommendations for Value Hypothesis

When formulating a value hypothesis , several key recommendations can significantly impact its effectiveness and the subsequent validation process.

Crafting a Good Hypothesis for Value Hypothesis

Clarity and specificity in value hypothesis.

A well-crafted value hypothesis should be clear, specific, and unambiguous. It must articulate the problem being addressed, the proposed solution, and the expected outcomes with precision to guide subsequent product development efforts effectively.

Testability and Measurability of Value Hypothesis

An effective value hypothesis should be testable and measurable. It should define success criteria that can be objectively evaluated, allowing startups to gather empirical evidence to validate whether their proposed solution genuinely creates value for their target audience.

Lean Startup Principles and Value Hypothesis

Embracing iterative development for value hypothesis.

Incorporating lean startup principles into the formulation of a value hypothesis involves embracing iterative development. This approach advocates rapid iteration, constant feedback loops, and validated learning to refine the value proposition based on real-time insights from users.

Customer-Centric Mindset in Value Hypothesis

A customer-centric mindset is pivotal when crafting a value hypothesis . Startups must prioritize understanding customer needs, preferences, and pain points to tailor their solutions effectively while aligning with lean principles of continuous improvement through user feedback.

Time and Resource Allocation for Value Hypothesis

Efficient resource management for value hypothesis.

Efficient resource allocation is crucial when validating a value hypothesis within the lean startup framework. Startups need to optimize resource allocation by focusing on high-impact activities that contribute to refining the value proposition based on validated learning.

Balancing Speed and Quality in Value Hypothesis

Balancing speed with quality is essential when validating a value hypothesis . While rapid iteration is encouraged within lean principles, startups must ensure that speed does not compromise the quality of insights gathered or the refinement process based on accurate data analysis.

Growth Hypothesis in Relation to Value Hypothesis

Establishing scalable sales strategies for value hypothesis.

The growth hypothesis examines how validated value propositions can impact product sales at scale. It focuses on identifying opportunities for sustainable revenue generation by leveraging a well-validated value hypothesis as an integral part of scalable sales strategies.

Repeatable Revenue Generation with Value Hypothesis

Validating a value hypothesis also involves testing its impact on repeatable revenue generation. By understanding how well the value proposition resonates with customers over time, startups can refine their offerings to ensure consistent revenue streams through sustained customer satisfaction.

About the Author : Quthor, powered by Quick Creator , is an AI writer that excels in creating high-quality articles from just a keyword or an idea. Leveraging Quick Creator's cutting-edge writing engine, Quthor efficiently gathers up-to-date facts and data to produce engaging and informative content. The article you're reading? Crafted by Quthor, demonstrating its capability to produce compelling content. Experience the power of AI writing. Try Quick Creator for free at quickcreator.io and start creating with Quthor today!

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Value Hypothesis 101: The Key to Building Products Customers Love

Ruben Buijs

Founder & Digital Consultant

Written on Aug 10, 2023

Product Management

A value hypothesis is an educated guess about the value a product will provide to its target customers. It involves making assumptions about how customers will benefit from using the product and then validating those assumptions through experimentation and customer feedback .

The goal is to prove that the product solves a real problem for customers before investing significant time and resources into building it out fully. A value hypothesis helps minimize the risk of launching a product that doesn't meet customer needs .

Let's consider an example to illustrate the concept of a value hypothesis. Imagine a team building a project management software. Their value hypothesis might be that by using their software, teams will be able to increase their productivity and efficiency by 30%. They would then design experiments and gather feedback from users to validate this hypothesis. If the feedback and data confirm the hypothesis, the team can be confident that their product delivers the promised value.

• How to Formulate a Strong Value Hypothesis

A good value hypothesis should be:

• Testable - It can be proven true or false based on evidence
• Precise - It clearly defines what success looks like in measurable terms
• Discrete - It focuses on testing one specific aspect of the product's value

Here's a template for structuring a value hypothesis:

"We believe that [customer segment] will [take this action/achieve this benefit] by using [ feature ] because [reason]."

• The Importance of Validating the Value Hypothesis

Validating the value hypothesis through customer research and experimentation is crucial before moving forward with product development. It helps:

• Avoid building a product no one wants
• Identify early adopters and understand their needs
• Iterate and improve the product based on real customer insights
• Gain confidence that you're investing resources in the right direction

Some ways to validate the hypothesis include customer interviews, surveys, prototype testing, and analyzing usage data from a minimum viable product (MVP).

• Value Hypothesis vs Growth Hypothesis

While a value hypothesis focuses on whether the product provides value to customers, a growth hypothesis looks at how customers will discover and adopt the product.

SaaS teams should validate the value hypothesis first to ensure they have a product worth growing, before moving on to test growth hypotheses around acquisition channels, pricing, virality, etc.

In summary, formulating and validating a value hypothesis is an essential step for SaaS product managers to de-risk product decisions, build customer-centric products, and lay the foundation for sustainable growth. It's about making your best educated guess and then letting real customer insights guide the way forward.

• How to Use Value Hypothesis

Define the value proposition : Clearly articulate the value your product aims to deliver to customers. This could be increased productivity, cost savings, improved collaboration, or any other benefit that aligns with your target audience's needs.

Make assumptions: Formulate specific assumptions about the value customers will derive from using your product. For example, you might assume that your product will help customers save 20 hours per week or reduce operational costs by 15%.

Design experiments: Create experiments to test your assumptions. These experiments could include user interviews, surveys, usability testing , or A/B testing. The goal is to collect data and feedback that either validates or disproves your value hypothesis.

Analyze and iterate: Analyze the data and feedback gathered from the experiments and iterate on your value hypothesis if necessary. If the data supports your assumptions, you can proceed with confidence. If not, adjust your hypothesis and repeat the experimentation process until you have a validated value proposition.

• Useful Tips

Involve customers early on: Engage with potential customers during the value hypothesis formulation stage to gain insights and validate assumptions before investing heavily in product development.

Test one assumption at a time: To ensure clarity and focus, test each assumption separately. This allows you to accurately identify which specific value proposition resonates most with your target audience.

Continuously gather feedback: Regularly collect feedback from customers to validate and refine your value hypothesis throughout the product lifecycle . This helps you adapt to changing market needs and stay ahead of the competition.

• Related Terms
• Product-Market Fit
• Minimum Viable Product (MVP)
• Customer Validation
• Value Proposition
• User Interviews
• Usability Testing
• A/B Testing
• Competitive Advantage

What is a value hypothesis?

Why is a value hypothesis important, how do you create a value hypothesis, what should a value hypothesis include, can a value hypothesis change over time, how can you validate a value hypothesis, what happens if a value hypothesis is proven wrong, can multiple value hypotheses coexist, should a value hypothesis be tested before development, how often should a value hypothesis be evaluated.

Ruben is the founder of ProductLift. I employ a decade of consulting experience from Ernst & Young to maximize clients' ROI on new Tech developments. I now help companies build better products

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S.3.2 hypothesis testing (p-value approach).

The P -value approach involves determining "likely" or "unlikely" by determining the probability — assuming the null hypothesis was true — of observing a more extreme test statistic in the direction of the alternative hypothesis than the one observed. If the P -value is small, say less than (or equal to) \(\alpha\), then it is "unlikely." And, if the P -value is large, say more than \(\alpha\), then it is "likely."

If the P -value is less than (or equal to) \(\alpha\), then the null hypothesis is rejected in favor of the alternative hypothesis. And, if the P -value is greater than \(\alpha\), then the null hypothesis is not rejected.

Specifically, the four steps involved in using the P -value approach to conducting any hypothesis test are:

• Specify the null and alternative hypotheses.
• Using the sample data and assuming the null hypothesis is true, calculate the value of the test statistic. Again, to conduct the hypothesis test for the population mean μ , we use the t -statistic \(t^*=\frac{\bar{x}-\mu}{s/\sqrt{n}}\) which follows a t -distribution with n - 1 degrees of freedom.
• Using the known distribution of the test statistic, calculate the P -value : "If the null hypothesis is true, what is the probability that we'd observe a more extreme test statistic in the direction of the alternative hypothesis than we did?" (Note how this question is equivalent to the question answered in criminal trials: "If the defendant is innocent, what is the chance that we'd observe such extreme criminal evidence?")
• Set the significance level, \(\alpha\), the probability of making a Type I error to be small — 0.01, 0.05, or 0.10. Compare the P -value to \(\alpha\). If the P -value is less than (or equal to) \(\alpha\), reject the null hypothesis in favor of the alternative hypothesis. If the P -value is greater than \(\alpha\), do not reject the null hypothesis.

Example S.3.2.1

Mean gpa section  .

In our example concerning the mean grade point average, suppose that our random sample of n = 15 students majoring in mathematics yields a test statistic t * equaling 2.5. Since n = 15, our test statistic t * has n - 1 = 14 degrees of freedom. Also, suppose we set our significance level α at 0.05 so that we have only a 5% chance of making a Type I error.

Right Tailed

The P -value for conducting the right-tailed test H 0 : μ = 3 versus H A : μ > 3 is the probability that we would observe a test statistic greater than t * = 2.5 if the population mean \(\mu\) really were 3. Recall that probability equals the area under the probability curve. The P -value is therefore the area under a t n - 1 = t 14 curve and to the right of the test statistic t * = 2.5. It can be shown using statistical software that the P -value is 0.0127. The graph depicts this visually.

The P -value, 0.0127, tells us it is "unlikely" that we would observe such an extreme test statistic t * in the direction of H A if the null hypothesis were true. Therefore, our initial assumption that the null hypothesis is true must be incorrect. That is, since the P -value, 0.0127, is less than \(\alpha\) = 0.05, we reject the null hypothesis H 0 : μ = 3 in favor of the alternative hypothesis H A : μ > 3.

Note that we would not reject H 0 : μ = 3 in favor of H A : μ > 3 if we lowered our willingness to make a Type I error to \(\alpha\) = 0.01 instead, as the P -value, 0.0127, is then greater than \(\alpha\) = 0.01.

Left Tailed

In our example concerning the mean grade point average, suppose that our random sample of n = 15 students majoring in mathematics yields a test statistic t * instead of equaling -2.5. The P -value for conducting the left-tailed test H 0 : μ = 3 versus H A : μ < 3 is the probability that we would observe a test statistic less than t * = -2.5 if the population mean μ really were 3. The P -value is therefore the area under a t n - 1 = t 14 curve and to the left of the test statistic t* = -2.5. It can be shown using statistical software that the P -value is 0.0127. The graph depicts this visually.

The P -value, 0.0127, tells us it is "unlikely" that we would observe such an extreme test statistic t * in the direction of H A if the null hypothesis were true. Therefore, our initial assumption that the null hypothesis is true must be incorrect. That is, since the P -value, 0.0127, is less than α = 0.05, we reject the null hypothesis H 0 : μ = 3 in favor of the alternative hypothesis H A : μ < 3.

Note that we would not reject H 0 : μ = 3 in favor of H A : μ < 3 if we lowered our willingness to make a Type I error to α = 0.01 instead, as the P -value, 0.0127, is then greater than \(\alpha\) = 0.01.

In our example concerning the mean grade point average, suppose again that our random sample of n = 15 students majoring in mathematics yields a test statistic t * instead of equaling -2.5. The P -value for conducting the two-tailed test H 0 : μ = 3 versus H A : μ ≠ 3 is the probability that we would observe a test statistic less than -2.5 or greater than 2.5 if the population mean μ really was 3. That is, the two-tailed test requires taking into account the possibility that the test statistic could fall into either tail (hence the name "two-tailed" test). The P -value is, therefore, the area under a t n - 1 = t 14 curve to the left of -2.5 and to the right of 2.5. It can be shown using statistical software that the P -value is 0.0127 + 0.0127, or 0.0254. The graph depicts this visually.

Note that the P -value for a two-tailed test is always two times the P -value for either of the one-tailed tests. The P -value, 0.0254, tells us it is "unlikely" that we would observe such an extreme test statistic t * in the direction of H A if the null hypothesis were true. Therefore, our initial assumption that the null hypothesis is true must be incorrect. That is, since the P -value, 0.0254, is less than α = 0.05, we reject the null hypothesis H 0 : μ = 3 in favor of the alternative hypothesis H A : μ ≠ 3.

Note that we would not reject H 0 : μ = 3 in favor of H A : μ ≠ 3 if we lowered our willingness to make a Type I error to α = 0.01 instead, as the P -value, 0.0254, is then greater than \(\alpha\) = 0.01.

Now that we have reviewed the critical value and P -value approach procedures for each of the three possible hypotheses, let's look at three new examples — one of a right-tailed test, one of a left-tailed test, and one of a two-tailed test.

The good news is that, whenever possible, we will take advantage of the test statistics and P -values reported in statistical software, such as Minitab, to conduct our hypothesis tests in this course.

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Statistics By Jim

Making statistics intuitive

Critical Value: Definition, Finding & Calculator

By Jim Frost 2 Comments

What is a Critical Value?

A critical value defines regions in the sampling distribution of a test statistic. These values play a role in both hypothesis tests and confidence intervals. In hypothesis tests, critical values determine whether the results are statistically significant. For confidence intervals, they help calculate the upper and lower limits.

In both cases, critical values account for uncertainty in sample data you’re using to make inferences about a population . They answer the following questions:

• How different does the sample estimate need to be from the null hypothesis to be statistically significant?
• What is the margin of error (confidence interval) around the sample estimate of the population parameter ?

In this post, I’ll show you how to find critical values, use them to determine statistical significance, and use them to construct confidence intervals. I also include a critical value calculator at the end of this article so you can apply what you learn.

Because most people start learning with the z-test and its test statistic, the z-score, I’ll use them for the examples throughout this post. However, I provide links with detailed information for other types of tests and sampling distributions.

Related posts : Sampling Distributions and Test Statistics

Using a Critical Value to Determine Statistical Significance

In this context, the sampling distribution of a test statistic defines the probability for ranges of values. The significance level (α) specifies the probability that corresponds with the critical value within the distribution. Let’s work through an example for a z-test.

The z-test uses the z test statistic. For this test, the z-distribution finds probabilities for ranges of z-scores under the assumption that the null hypothesis is true. For a z-test, the null z-score is zero, which is at the central peak of the sampling distribution. This sampling distribution centers on the null hypothesis value, and the critical values mark the minimum distance from the null hypothesis required for statistical significance.

Critical values depend on your significance level and whether you’re performing a one- or two-sided hypothesis. For these examples, I’ll use a significance level of 0.05. This value defines how improbable the test statistic must be to be significant.

Related posts : Significance Levels and P-values and Z-scores

Two-Sided Tests

Two-sided hypothesis tests have two rejection regions. Consequently, you’ll need two critical values that define them. Because there are two rejection regions, we must split our significance level in half. Each rejection region has a probability of α / 2, making the total likelihood for both areas equal the significance level.

The probability plot below displays the critical values and the rejection regions for a two-sided z-test with a significance level of 0.05. When the z-score is ≤ -1.96 or ≥ 1.96, it exceeds the cutoff, and your results are statistically significant.

One-Sided Tests

One-tailed tests have one rejection region and, hence, only one critical value. The total α probability goes into that one side. The probability plots below display these values for right- and left-sided z-tests. These tests can detect effects in only one direction.

Related post : Understanding One-Tailed and Two-Tailed Hypothesis Tests and Effects in Statistics

Using a Critical Value to Construct Confidence Intervals

Confidence intervals use the same critical values (CVs) as the corresponding hypothesis test. The confidence level equals 1 – the significance level. Consequently, the CVs for a significance level of 0.05 produce a confidence level of 1 – 0.05 = 0.95 or 95%.

For example, to calculate the 95% confidence interval for our two-tailed z-test with a significance level of 0.05, use the CVs of -1.96 and 1.96 that we found above.

To calculate the upper and lower limits of the interval, take the positive critical value and multiply it by the standard error of the mean. Then take the sample mean and add and subtract that product from it.

• Lower Limit = Sample Mean – (CV * Standard Error of the Mean)
• Upper Limit = Sample Mean + (CV * Standard Error of the Mean)

To learn more about confidence intervals and how to construct them, read my posts about Confidence Intervals and How Confidence Intervals Work .

Related post : Standard Error of the Mean

How to Find a Critical Value

Unfortunately, the formulas for finding critical values are very complex. Typically, you don’t calculate them by hand. For the examples in this article, I’ve used statistical software to find them. However, you can also use statistical tables.

To learn how to use these critical value tables, read my articles that contain the tables and information about using them. The process for finding them is similar for the various tests. Using these tables requires knowing the correct test statistic, the significance level, the number of tails, and, in most cases, the degrees of freedom.

The following articles provide the statistical tables, explain how to use them, and visually illustrate the results.

• T distribution table
• Chi-square table

Related post : Degrees of Freedom

Critical Value Calculator

Another method for finding CVs is to use a critical value calculator, such as the one below. These calculators are handy for finding the answer, but they don’t provide the context for the results.

This calculator finds critical values for the sampling distributions of common test statistics.

For example, choose the following in the calculator:

• Z (standard normal)
• Significance level = 0.05

The calculator will display the same ±1.96 values we found earlier in this article.

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Reader Interactions

January 16, 2024 at 5:26 pm

Hello, I am currently taking statistics and am reviewing confidence intervals. I would like to know what is the equation for calculating a two-tailed test for upper and lower limits? I would like to know is there a way to calculate one and two-tailed tests without using a confidence interval calculator and can you explain further?

January 16, 2024 at 6:43 pm

If you’re talking about calculating the critical values values for a test statistic for two-tailed test, the calculations are fairly complex. Consequently, you’ll either use statistical software, an online calculator, or a statistical table to find those limits.

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StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

StatPearls [Internet].

Hypothesis testing, p values, confidence intervals, and significance.

Jacob Shreffler ; Martin R. Huecker .

Affiliations

Last Update: March 13, 2023 .

• Definition/Introduction

Medical providers often rely on evidence-based medicine to guide decision-making in practice. Often a research hypothesis is tested with results provided, typically with p values, confidence intervals, or both. Additionally, statistical or research significance is estimated or determined by the investigators. Unfortunately, healthcare providers may have different comfort levels in interpreting these findings, which may affect the adequate application of the data.

• Issues of Concern

Without a foundational understanding of hypothesis testing, p values, confidence intervals, and the difference between statistical and clinical significance, it may affect healthcare providers' ability to make clinical decisions without relying purely on the research investigators deemed level of significance. Therefore, an overview of these concepts is provided to allow medical professionals to use their expertise to determine if results are reported sufficiently and if the study outcomes are clinically appropriate to be applied in healthcare practice.

Hypothesis Testing

Investigators conducting studies need research questions and hypotheses to guide analyses. Starting with broad research questions (RQs), investigators then identify a gap in current clinical practice or research. Any research problem or statement is grounded in a better understanding of relationships between two or more variables. For this article, we will use the following research question example:

Research Question: Is Drug 23 an effective treatment for Disease A?

Research questions do not directly imply specific guesses or predictions; we must formulate research hypotheses. A hypothesis is a predetermined declaration regarding the research question in which the investigator(s) makes a precise, educated guess about a study outcome. This is sometimes called the alternative hypothesis and ultimately allows the researcher to take a stance based on experience or insight from medical literature. An example of a hypothesis is below.

Research Hypothesis: Drug 23 will significantly reduce symptoms associated with Disease A compared to Drug 22.

The null hypothesis states that there is no statistical difference between groups based on the stated research hypothesis.

Researchers should be aware of journal recommendations when considering how to report p values, and manuscripts should remain internally consistent.

Regarding p values, as the number of individuals enrolled in a study (the sample size) increases, the likelihood of finding a statistically significant effect increases. With very large sample sizes, the p-value can be very low significant differences in the reduction of symptoms for Disease A between Drug 23 and Drug 22. The null hypothesis is deemed true until a study presents significant data to support rejecting the null hypothesis. Based on the results, the investigators will either reject the null hypothesis (if they found significant differences or associations) or fail to reject the null hypothesis (they could not provide proof that there were significant differences or associations).

To test a hypothesis, researchers obtain data on a representative sample to determine whether to reject or fail to reject a null hypothesis. In most research studies, it is not feasible to obtain data for an entire population. Using a sampling procedure allows for statistical inference, though this involves a certain possibility of error. [1]  When determining whether to reject or fail to reject the null hypothesis, mistakes can be made: Type I and Type II errors. Though it is impossible to ensure that these errors have not occurred, researchers should limit the possibilities of these faults. [2]

Significance

Significance is a term to describe the substantive importance of medical research. Statistical significance is the likelihood of results due to chance. [3]  Healthcare providers should always delineate statistical significance from clinical significance, a common error when reviewing biomedical research. [4]  When conceptualizing findings reported as either significant or not significant, healthcare providers should not simply accept researchers' results or conclusions without considering the clinical significance. Healthcare professionals should consider the clinical importance of findings and understand both p values and confidence intervals so they do not have to rely on the researchers to determine the level of significance. [5]  One criterion often used to determine statistical significance is the utilization of p values.

P values are used in research to determine whether the sample estimate is significantly different from a hypothesized value. The p-value is the probability that the observed effect within the study would have occurred by chance if, in reality, there was no true effect. Conventionally, data yielding a p<0.05 or p<0.01 is considered statistically significant. While some have debated that the 0.05 level should be lowered, it is still universally practiced. [6]  Hypothesis testing allows us to determine the size of the effect.

An example of findings reported with p values are below:

Statement: Drug 23 reduced patients' symptoms compared to Drug 22. Patients who received Drug 23 (n=100) were 2.1 times less likely than patients who received Drug 22 (n = 100) to experience symptoms of Disease A, p<0.05.

Statement:Individuals who were prescribed Drug 23 experienced fewer symptoms (M = 1.3, SD = 0.7) compared to individuals who were prescribed Drug 22 (M = 5.3, SD = 1.9). This finding was statistically significant, p= 0.02.

For either statement, if the threshold had been set at 0.05, the null hypothesis (that there was no relationship) should be rejected, and we should conclude significant differences. Noticeably, as can be seen in the two statements above, some researchers will report findings with < or > and others will provide an exact p-value (0.000001) but never zero [6] . When examining research, readers should understand how p values are reported. The best practice is to report all p values for all variables within a study design, rather than only providing p values for variables with significant findings. [7]  The inclusion of all p values provides evidence for study validity and limits suspicion for selective reporting/data mining.  

While researchers have historically used p values, experts who find p values problematic encourage the use of confidence intervals. [8] . P-values alone do not allow us to understand the size or the extent of the differences or associations. [3]  In March 2016, the American Statistical Association (ASA) released a statement on p values, noting that scientific decision-making and conclusions should not be based on a fixed p-value threshold (e.g., 0.05). They recommend focusing on the significance of results in the context of study design, quality of measurements, and validity of data. Ultimately, the ASA statement noted that in isolation, a p-value does not provide strong evidence. [9]

When conceptualizing clinical work, healthcare professionals should consider p values with a concurrent appraisal study design validity. For example, a p-value from a double-blinded randomized clinical trial (designed to minimize bias) should be weighted higher than one from a retrospective observational study [7] . The p-value debate has smoldered since the 1950s [10] , and replacement with confidence intervals has been suggested since the 1980s. [11]

Confidence Intervals

A confidence interval provides a range of values within given confidence (e.g., 95%), including the accurate value of the statistical constraint within a targeted population. [12]  Most research uses a 95% CI, but investigators can set any level (e.g., 90% CI, 99% CI). [13]  A CI provides a range with the lower bound and upper bound limits of a difference or association that would be plausible for a population. [14]  Therefore, a CI of 95% indicates that if a study were to be carried out 100 times, the range would contain the true value in 95, [15]  confidence intervals provide more evidence regarding the precision of an estimate compared to p-values. [6]

In consideration of the similar research example provided above, one could make the following statement with 95% CI:

Statement: Individuals who were prescribed Drug 23 had no symptoms after three days, which was significantly faster than those prescribed Drug 22; there was a mean difference between the two groups of days to the recovery of 4.2 days (95% CI: 1.9 – 7.8).

It is important to note that the width of the CI is affected by the standard error and the sample size; reducing a study sample number will result in less precision of the CI (increase the width). [14]  A larger width indicates a smaller sample size or a larger variability. [16]  A researcher would want to increase the precision of the CI. For example, a 95% CI of 1.43 – 1.47 is much more precise than the one provided in the example above. In research and clinical practice, CIs provide valuable information on whether the interval includes or excludes any clinically significant values. [14]

Null values are sometimes used for differences with CI (zero for differential comparisons and 1 for ratios). However, CIs provide more information than that. [15]  Consider this example: A hospital implements a new protocol that reduced wait time for patients in the emergency department by an average of 25 minutes (95% CI: -2.5 – 41 minutes). Because the range crosses zero, implementing this protocol in different populations could result in longer wait times; however, the range is much higher on the positive side. Thus, while the p-value used to detect statistical significance for this may result in "not significant" findings, individuals should examine this range, consider the study design, and weigh whether or not it is still worth piloting in their workplace.

Similarly to p-values, 95% CIs cannot control for researchers' errors (e.g., study bias or improper data analysis). [14]  In consideration of whether to report p-values or CIs, researchers should examine journal preferences. When in doubt, reporting both may be beneficial. [13]  An example is below:

Reporting both: Individuals who were prescribed Drug 23 had no symptoms after three days, which was significantly faster than those prescribed Drug 22, p = 0.009. There was a mean difference between the two groups of days to the recovery of 4.2 days (95% CI: 1.9 – 7.8).

• Clinical Significance

Recall that clinical significance and statistical significance are two different concepts. Healthcare providers should remember that a study with statistically significant differences and large sample size may be of no interest to clinicians, whereas a study with smaller sample size and statistically non-significant results could impact clinical practice. [14]  Additionally, as previously mentioned, a non-significant finding may reflect the study design itself rather than relationships between variables.

Healthcare providers using evidence-based medicine to inform practice should use clinical judgment to determine the practical importance of studies through careful evaluation of the design, sample size, power, likelihood of type I and type II errors, data analysis, and reporting of statistical findings (p values, 95% CI or both). [4]  Interestingly, some experts have called for "statistically significant" or "not significant" to be excluded from work as statistical significance never has and will never be equivalent to clinical significance. [17]

The decision on what is clinically significant can be challenging, depending on the providers' experience and especially the severity of the disease. Providers should use their knowledge and experiences to determine the meaningfulness of study results and make inferences based not only on significant or insignificant results by researchers but through their understanding of study limitations and practical implications.

• Nursing, Allied Health, and Interprofessional Team Interventions

All physicians, nurses, pharmacists, and other healthcare professionals should strive to understand the concepts in this chapter. These individuals should maintain the ability to review and incorporate new literature for evidence-based and safe care. 

• Review Questions
• Access free multiple choice questions on this topic.
• Comment on this article.

Disclosure: Jacob Shreffler declares no relevant financial relationships with ineligible companies.

Disclosure: Martin Huecker declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

• Cite this Page Shreffler J, Huecker MR. Hypothesis Testing, P Values, Confidence Intervals, and Significance. [Updated 2023 Mar 13]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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P-Value in Statistical Hypothesis Tests: What is it?

P value definition.

A p value is used in hypothesis testing to help you support or reject the null hypothesis . The p value is the evidence against a null hypothesis . The smaller the p-value, the stronger the evidence that you should reject the null hypothesis.

P values are expressed as decimals although it may be easier to understand what they are if you convert them to a percentage . For example, a p value of 0.0254 is 2.54%. This means there is a 2.54% chance your results could be random (i.e. happened by chance). That’s pretty tiny. On the other hand, a large p-value of .9(90%) means your results have a 90% probability of being completely random and not due to anything in your experiment. Therefore, the smaller the p-value, the more important (“ significant “) your results.

When you run a hypothesis test , you compare the p value from your test to the alpha level you selected when you ran the test. Alpha levels can also be written as percentages.

P Value vs Alpha level

Alpha levels are controlled by the researcher and are related to confidence levels . You get an alpha level by subtracting your confidence level from 100%. For example, if you want to be 98 percent confident in your research, the alpha level would be 2% (100% – 98%). When you run the hypothesis test, the test will give you a value for p. Compare that value to your chosen alpha level. For example, let’s say you chose an alpha level of 5% (0.05). If the results from the test give you:

• A small p (≤ 0.05), reject the null hypothesis . This is strong evidence that the null hypothesis is invalid.
• A large p (> 0.05) means the alternate hypothesis is weak, so you do not reject the null.

P Values and Critical Values

What if I Don’t Have an Alpha Level?

In an ideal world, you’ll have an alpha level. But if you do not, you can still use the following rough guidelines in deciding whether to support or reject the null hypothesis:

• If p > .10 → “not significant”
• If p ≤ .10 → “marginally significant”
• If p ≤ .05 → “significant”
• If p ≤ .01 → “highly significant.”

How to Calculate a P Value on the TI 83

Example question: The average wait time to see an E.R. doctor is said to be 150 minutes. You think the wait time is actually less. You take a random sample of 30 people and find their average wait is 148 minutes with a standard deviation of 5 minutes. Assume the distribution is normal. Find the p value for this test.

• Press STAT then arrow over to TESTS.
• Press ENTER for Z-Test .
• Arrow over to Stats. Press ENTER.
• Arrow down to μ0 and type 150. This is our null hypothesis mean.
• Arrow down to σ. Type in your std dev: 5.
• Arrow down to xbar. Type in your sample mean : 148.
• Arrow down to n. Type in your sample size : 30.
• Arrow to <μ0 for a left tail test . Press ENTER.
• Arrow down to Calculate. Press ENTER. P is given as .014, or about 1%.

The probability that you would get a sample mean of 148 minutes is tiny, so you should reject the null hypothesis.

Note : If you don’t want to run a test, you could also use the TI 83 NormCDF function to get the area (which is the same thing as the probability value).

Dodge, Y. (2008). The Concise Encyclopedia of Statistics . Springer. Gonick, L. (1993). The Cartoon Guide to Statistics . HarperPerennial.

P-value Calculator

Statistical significance calculator to easily calculate the p-value and determine whether the difference between two proportions or means (independent groups) is statistically significant. T-test calculator & z-test calculator to compute the Z-score or T-score for inference about absolute or relative difference (percentage change, percent effect). Suitable for analysis of simple A/B tests.

Related calculators

• Using the p-value calculator
• What is "p-value" and "significance level"
• P-value formula
• Why do we need a p-value?
• How to interpret a statistically significant result / low p-value
• P-value and significance for relative difference in means or proportions

    Using the p-value calculator

This statistical significance calculator allows you to perform a post-hoc statistical evaluation of a set of data when the outcome of interest is difference of two proportions (binomial data, e.g. conversion rate or event rate) or difference of two means (continuous data, e.g. height, weight, speed, time, revenue, etc.). You can use a Z-test (recommended) or a T-test to find the observed significance level (p-value statistic). The Student's T-test is recommended mostly for very small sample sizes, e.g. n < 30. In order to avoid type I error inflation which might occur with unequal variances the calculator automatically applies the Welch's T-test instead of Student's T-test if the sample sizes differ significantly or if one of them is less than 30 and the sampling ratio is different than one.

If entering proportions data, you need to know the sample sizes of the two groups as well as the number or rate of events. These can be entered as proportions (e.g. 0.10), percentages (e.g. 10%) or just raw numbers of events (e.g. 50).

If entering means data, simply copy/paste or type in the raw data, each observation separated by comma, space, new line or tab. Copy-pasting from a Google or Excel spreadsheet works fine.

The p-value calculator will output : p-value, significance level, T-score or Z-score (depending on the choice of statistical hypothesis test), degrees of freedom, and the observed difference. For means data it will also output the sample sizes, means, and pooled standard error of the mean. The p-value is for a one-sided hypothesis (one-tailed test), allowing you to infer the direction of the effect (more on one vs. two-tailed tests ). However, the probability value for the two-sided hypothesis (two-tailed p-value) is also calculated and displayed, although it should see little to no practical applications.

Warning: You must have fixed the sample size / stopping time of your experiment in advance, otherwise you will be guilty of optional stopping (fishing for significance) which will inflate the type I error of the test rendering the statistical significance level unusable. Also, you should not use this significance calculator for comparisons of more than two means or proportions, or for comparisons of two groups based on more than one metric. If a test involves more than one treatment group or more than one outcome variable you need a more advanced tool which corrects for multiple comparisons and multiple testing. This statistical calculator might help.

    What is "p-value" and "significance level"

The p-value is a heavily used test statistic that quantifies the uncertainty of a given measurement, usually as a part of an experiment, medical trial, as well as in observational studies. By definition, it is inseparable from inference through a Null-Hypothesis Statistical Test (NHST) . In it we pose a null hypothesis reflecting the currently established theory or a model of the world we don't want to dismiss without solid evidence (the tested hypothesis), and an alternative hypothesis: an alternative model of the world. For example, the statistical null hypothesis could be that exposure to ultraviolet light for prolonged periods of time has positive or neutral effects regarding developing skin cancer, while the alternative hypothesis can be that it has a negative effect on development of skin cancer.

In this framework a p-value is defined as the probability of observing the result which was observed, or a more extreme one, assuming the null hypothesis is true . In notation this is expressed as:

p(x 0 ) = Pr(d(X) > d(x 0 ); H 0 )

where x 0 is the observed data (x 1 ,x 2 ...x n ), d is a special function (statistic, e.g. calculating a Z-score), X is a random sample (X 1 ,X 2 ...X n ) from the sampling distribution of the null hypothesis. This equation is used in this p-value calculator and can be visualized as such:

Therefore the p-value expresses the probability of committing a type I error : rejecting the null hypothesis if it is in fact true. See below for a full proper interpretation of the p-value statistic .

Another way to think of the p-value is as a more user-friendly expression of how many standard deviations away from the normal a given observation is. For example, in a one-tailed test of significance for a normally-distributed variable like the difference of two means, a result which is 1.6448 standard deviations away (1.6448σ) results in a p-value of 0.05.

The term "statistical significance" or "significance level" is often used in conjunction to the p-value, either to say that a result is "statistically significant", which has a specific meaning in statistical inference ( see interpretation below ), or to refer to the percentage representation the level of significance: (1 - p value), e.g. a p-value of 0.05 is equivalent to significance level of 95% (1 - 0.05 * 100). A significance level can also be expressed as a T-score or Z-score, e.g. a result would be considered significant only if the Z-score is in the critical region above 1.96 (equivalent to a p-value of 0.025).

    P-value formula

There are different ways to arrive at a p-value depending on the assumption about the underlying distribution. This tool supports two such distributions: the Student's T-distribution and the normal Z-distribution (Gaussian) resulting in a T test and a Z test, respectively.

In both cases, to find the p-value start by estimating the variance and standard deviation, then derive the standard error of the mean, after which a standard score is found using the formula [2] :

X (read "X bar") is the arithmetic mean of the population baseline or the control, μ 0 is the observed mean / treatment group mean, while σ x is the standard error of the mean (SEM, or standard deviation of the error of the mean).

When calculating a p-value using the Z-distribution the formula is Φ(Z) or Φ(-Z) for lower and upper-tailed tests, respectively. Φ is the standard normal cumulative distribution function and a Z-score is computed. In this mode the tool functions as a Z score calculator.

When using the T-distribution the formula is T n (Z) or T n (-Z) for lower and upper-tailed tests, respectively. T n is the cumulative distribution function for a T-distribution with n degrees of freedom and so a T-score is computed. Selecting this mode makes the tool behave as a T test calculator.

The population standard deviation is often unknown and is thus estimated from the samples, usually from the pooled samples variance. Knowing or estimating the standard deviation is a prerequisite for using a significance calculator. Note that differences in means or proportions are normally distributed according to the Central Limit Theorem (CLT) hence a Z-score is the relevant statistic for such a test.

    Why do we need a p-value?

If you are in the sciences, it is often a requirement by scientific journals. If you apply in business experiments (e.g. A/B testing) it is reported alongside confidence intervals and other estimates. However, what is the utility of p-values and by extension that of significance levels?

First, let us define the problem the p-value is intended to solve. People need to share information about the evidential strength of data that can be easily understood and easily compared between experiments. The picture below represents, albeit imperfectly, the results of two simple experiments, each ending up with the control with 10% event rate treatment group at 12% event rate.

However, it is obvious that the evidential input of the data is not the same, demonstrating that communicating just the observed proportions or their difference (effect size) is not enough to estimate and communicate the evidential strength of the experiment. In order to fully describe the evidence and associated uncertainty , several statistics need to be communicated, for example, the sample size, sample proportions and the shape of the error distribution. Their interaction is not trivial to understand, so communicating them separately makes it very difficult for one to grasp what information is present in the data. What would you infer if told that the observed proportions are 0.1 and 0.12 (e.g. conversion rate of 10% and 12%), the sample sizes are 10,000 users each, and the error distribution is binomial?

Instead of communicating several statistics, a single statistic was developed that communicates all the necessary information in one piece: the p-value . A p-value was first derived in the late 18-th century by Pierre-Simon Laplace, when he observed data about a million births that showed an excess of boys, compared to girls. Using the calculation of significance he argued that the effect was real but unexplained at the time. We know this now to be true and there are several explanations for the phenomena coming from evolutionary biology. Statistical significance calculations were formally introduced in the early 20-th century by Pearson and popularized by Sir Ronald Fisher in his work, most notably "The Design of Experiments" (1935) [1] in which p-values were featured extensively. In business settings significance levels and p-values see widespread use in process control and various business experiments (such as online A/B tests, i.e. as part of conversion rate optimization, marketing optimization, etc.).

    How to interpret a statistically significant result / low p-value

Saying that a result is statistically significant means that the p-value is below the evidential threshold (significance level) decided for the statistical test before it was conducted. For example, if observing something which would only happen 1 out of 20 times if the null hypothesis is true is considered sufficient evidence to reject the null hypothesis, the threshold will be 0.05. In such case, observing a p-value of 0.025 would mean that the result is interpreted as statistically significant.

But what does that really mean? What inference can we make from seeing a result which was quite improbable if the null was true?

Observing any given low p-value can mean one of three things [3] :

• There is a true effect from the tested treatment or intervention.
• There is no true effect, but we happened to observe a rare outcome. The lower the p-value, the rarer (less likely, less probable) the outcome.
• The statistical model is invalid (does not reflect reality).

Obviously, one can't simply jump to conclusion 1.) and claim it with one hundred percent certainty, as this would go against the whole idea of the p-value and statistical significance. In order to use p-values as a part of a decision process external factors part of the experimental design process need to be considered which includes deciding on the significance level (threshold), sample size and power (power analysis), and the expected effect size, among other things. If you are happy going forward with this much (or this little) uncertainty as is indicated by the p-value calculation suggests, then you have some quantifiable guarantees related to the effect and future performance of whatever you are testing, e.g. the efficacy of a vaccine or the conversion rate of an online shopping cart.

Note that it is incorrect to state that a Z-score or a p-value obtained from any statistical significance calculator tells how likely it is that the observation is "due to chance" or conversely - how unlikely it is to observe such an outcome due to "chance alone". P-values are calculated under specified statistical models hence 'chance' can be used only in reference to that specific data generating mechanism and has a technical meaning quite different from the colloquial one. For a deeper take on the p-value meaning and interpretation, including common misinterpretations, see: definition and interpretation of the p-value in statistics .

    P-value and significance for relative difference in means or proportions

When comparing two independent groups and the variable of interest is the relative (a.k.a. relative change, relative difference, percent change, percentage difference), as opposed to the absolute difference between the two means or proportions, the standard deviation of the variable is different which compels a different way of calculating p-values [5] . The need for a different statistical test is due to the fact that in calculating relative difference involves performing an additional division by a random variable: the event rate of the control during the experiment which adds more variance to the estimation and the resulting statistical significance is usually higher (the result will be less statistically significant). What this means is that p-values from a statistical hypothesis test for absolute difference in means would nominally meet the significance level, but they will be inadequate given the statistical inference for the hypothesis at hand.

In simulations I performed the difference in p-values was about 50% of nominal: a 0.05 p-value for absolute difference corresponded to probability of about 0.075 of observing the relative difference corresponding to the observed absolute difference. Therefore, if you are using p-values calculated for absolute difference when making an inference about percentage difference, you are likely reporting error rates which are about 50% of the actual, thus significantly overstating the statistical significance of your results and underestimating the uncertainty attached to them.

In short - switching from absolute to relative difference requires a different statistical hypothesis test. With this calculator you can avoid the mistake of using the wrong test simply by indicating the inference you want to make.

    References

1 Fisher R.A. (1935) – "The Design of Experiments", Edinburgh: Oliver & Boyd

2 Mayo D.G., Spanos A. (2010) – "Error Statistics", in P. S. Bandyopadhyay & M. R. Forster (Eds.), Philosophy of Statistics, (7, 152–198). Handbook of the Philosophy of Science . The Netherlands: Elsevier.

3 Georgiev G.Z. (2017) "Statistical Significance in A/B Testing – a Complete Guide", [online] https://blog.analytics-toolkit.com/2017/statistical-significance-ab-testing-complete-guide/ (accessed Apr 27, 2018)

4 Mayo D.G., Spanos A. (2006) – "Severe Testing as a Basic Concept in a Neyman–Pearson Philosophy of Induction", British Society for the Philosophy of Science , 57:323-357

5 Georgiev G.Z. (2018) "Confidence Intervals & P-values for Percent Change / Relative Difference", [online] https://blog.analytics-toolkit.com/2018/confidence-intervals-p-values-percent-change-relative-difference/ (accessed May 20, 2018)

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If you'd like to cite this online calculator resource and information as provided on the page, you can use the following citation: Georgiev G.Z., "P-value Calculator" , [online] Available at: https://www.gigacalculator.com/calculators/p-value-significance-calculator.php URL [Accessed Date: 21 Aug, 2024].

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• Chi-Square (Χ²) Tests | Types, Formula & Examples

Chi-Square (Χ²) Tests | Types, Formula & Examples

Published on May 23, 2022 by Shaun Turney . Revised on June 22, 2023.

A Pearson’s chi-square test is a statistical test for categorical data. It is used to determine whether your data are significantly different from what you expected. There are two types of Pearson’s chi-square tests:

• The chi-square goodness of fit test is used to test whether the frequency distribution of a categorical variable is different from your expectations.
• The chi-square test of independence is used to test whether two categorical variables are related to each other.

Table of contents

What is a chi-square test, the chi-square formula, when to use a chi-square test, types of chi-square tests, how to perform a chi-square test, how to report a chi-square test, practice questions, other interesting articles, frequently asked questions about chi-square tests.

Pearson’s chi-square (Χ 2 ) tests, often referred to simply as chi-square tests, are among the most common nonparametric tests . Nonparametric tests are used for data that don’t follow the assumptions of parametric tests , especially the assumption of a normal distribution .

If you want to test a hypothesis about the distribution of a categorical variable you’ll need to use a chi-square test or another nonparametric test. Categorical variables can be nominal or ordinal and represent groupings such as species or nationalities. Because they can only have a few specific values, they can’t have a normal distribution.

Test hypotheses about frequency distributions

There are two types of Pearson’s chi-square tests, but they both test whether the observed frequency distribution of a categorical variable is significantly different from its expected frequency distribution. A frequency distribution describes how observations are distributed between different groups.

Frequency distributions are often displayed using frequency distribution tables . A frequency distribution table shows the number of observations in each group. When there are two categorical variables, you can use a specific type of frequency distribution table called a contingency table to show the number of observations in each combination of groups.

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Both of Pearson’s chi-square tests use the same formula to calculate the test statistic , chi-square (Χ 2 ):

• Χ 2 is the chi-square test statistic
• Σ is the summation operator (it means “take the sum of”)
• O is the observed frequency
• E is the expected frequency

The larger the difference between the observations and the expectations ( O − E in the equation), the bigger the chi-square will be. To decide whether the difference is big enough to be statistically significant , you compare the chi-square value to a critical value.

A Pearson’s chi-square test may be an appropriate option for your data if all of the following are true:

• You want to test a hypothesis about one or more categorical variables . If one or more of your variables is quantitative, you should use a different statistical test . Alternatively, you could convert the quantitative variable into a categorical variable by separating the observations into intervals.
• The sample was randomly selected from the population .
• There are a minimum of five observations expected in each group or combination of groups.

The two types of Pearson’s chi-square tests are:

Chi-square goodness of fit test

Chi-square test of independence.

Mathematically, these are actually the same test. However, we often think of them as different tests because they’re used for different purposes.

You can use a chi-square goodness of fit test when you have one categorical variable. It allows you to test whether the frequency distribution of the categorical variable is significantly different from your expectations. Often, but not always, the expectation is that the categories will have equal proportions.

• Null hypothesis ( H 0 ): The bird species visit the bird feeder in equal proportions.
• Alternative hypothesis ( H A ): The bird species visit the bird feeder in different proportions.

Expectation of different proportions

• Null hypothesis ( H 0 ): The bird species visit the bird feeder in the same proportions as the average over the past five years.
• Alternative hypothesis ( H A ): The bird species visit the bird feeder in different proportions from the average over the past five years.

You can use a chi-square test of independence when you have two categorical variables. It allows you to test whether the two variables are related to each other. If two variables are independent (unrelated), the probability of belonging to a certain group of one variable isn’t affected by the other variable .

• Null hypothesis ( H 0 ): The proportion of people who are left-handed is the same for Americans and Canadians.
• Alternative hypothesis ( H A ): The proportion of people who are left-handed differs between nationalities.

Other types of chi-square tests

Some consider the chi-square test of homogeneity to be another variety of Pearson’s chi-square test. It tests whether two populations come from the same distribution by determining whether the two populations have the same proportions as each other. You can consider it simply a different way of thinking about the chi-square test of independence.

McNemar’s test is a test that uses the chi-square test statistic. It isn’t a variety of Pearson’s chi-square test, but it’s closely related. You can conduct this test when you have a related pair of categorical variables that each have two groups. It allows you to determine whether the proportions of the variables are equal.

• Null hypothesis ( H 0 ): The proportion of people who like chocolate is the same as the proportion of people who like vanilla.
• Alternative hypothesis ( H A ): The proportion of people who like chocolate is different from the proportion of people who like vanilla.

There are several other types of chi-square tests that are not Pearson’s chi-square tests, including the test of a single variance and the likelihood ratio chi-square test .

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The exact procedure for performing a Pearson’s chi-square test depends on which test you’re using, but it generally follows these steps:

• Create a table of the observed and expected frequencies. This can sometimes be the most difficult step because you will need to carefully consider which expected values are most appropriate for your null hypothesis.
• Calculate the chi-square value from your observed and expected frequencies using the chi-square formula.
• Find the critical chi-square value in a chi-square critical value table or using statistical software.
• Compare the chi-square value to the critical value to determine which is larger.
• Decide whether to reject the null hypothesis. You should reject the null hypothesis if the chi-square value is greater than the critical value. If you reject the null hypothesis, you can conclude that your data are significantly different from what you expected.

If you decide to include a Pearson’s chi-square test in your research paper , dissertation or thesis , you should report it in your results section . You can follow these rules if you want to report statistics in APA Style :

• You don’t need to provide a reference or formula since the chi-square test is a commonly used statistic.
• Refer to chi-square using its Greek symbol, Χ 2 . Although the symbol looks very similar to an “X” from the Latin alphabet, it’s actually a different symbol. Greek symbols should not be italicized.
• Include a space on either side of the equal sign.
• If your chi-square is less than zero, you should include a leading zero (a zero before the decimal point) since the chi-square can be greater than zero.
• Provide two significant digits after the decimal point.
• Report the chi-square alongside its degrees of freedom , sample size, and p value , following this format: Χ 2 (degrees of freedom, N = sample size) = chi-square value, p = p value).

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

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis

Methodology

• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

The two main chi-square tests are the chi-square goodness of fit test and the chi-square test of independence .

Both chi-square tests and t tests can test for differences between two groups. However, a t test is used when you have a dependent quantitative variable and an independent categorical variable (with two groups). A chi-square test of independence is used when you have two categorical variables.

Both correlations and chi-square tests can test for relationships between two variables. However, a correlation is used when you have two quantitative variables and a chi-square test of independence is used when you have two categorical variables.

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

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