what does product hypothesis mean

How to Generate and Validate Product Hypotheses

Every product owner knows that it takes effort to build something that'll cater to user needs. You'll have to make many tough calls if you wish to grow the company and evolve the product so it delivers more value. But how do you decide what to change in the product, your marketing strategy, or the overall direction to succeed? And how do you make a product that truly resonates with your target audience?

There are many unknowns in business, so many fundamental decisions start from a simple "what if?". But they can't be based on guesses, as you need some proof to fill in the blanks reasonably.

Because there's no universal recipe for successfully building a product, teams collect data, do research, study the dynamics, and generate hypotheses according to the given facts. They then take corresponding actions to find out whether they were right or wrong, make conclusions, and most likely restart the process again.

On this page, we thoroughly inspect product hypotheses. We'll go over what they are, how to create hypothesis statements and validate them, and what goes after this step.

What Is a Hypothesis in Product Management?

A hypothesis in product development and product management is a statement or assumption about the product, planned feature, market, or customer (e.g., their needs, behavior, or expectations) that you can put to the test, evaluate, and base your further decisions on . This may, for instance, regard the upcoming product changes as well as the impact they can result in.

A hypothesis implies that there is limited knowledge. Hence, the teams need to undergo testing activities to validate their ideas and confirm whether they are true or false.

Hypotheses guide the product development process and may point at important findings to help build a better product that'll serve user needs. In essence, teams create hypothesis statements in an attempt to improve the offering, boost engagement, increase revenue, find product-market fit quicker, or for other business-related reasons.

It's sort of like an experiment with trial and error, yet, it is data-driven and should be unbiased . This means that teams don't make assumptions out of the blue. Instead, they turn to the collected data, conducted market research , and factual information, which helps avoid completely missing the mark. The obtained results are then carefully analyzed and may influence decision-making.

Such experiments backed by data and analysis are an integral aspect of successful product development and allow startups or businesses to dodge costly startup mistakes .

‍ When do teams create hypothesis statements and validate them? To some extent, hypothesis testing is an ongoing process to work on constantly. It may occur during various product development life cycle stages, from early phases like initiation to late ones like scaling.

In any event, the key here is learning how to generate hypothesis statements and validate them effectively. We'll go over this in more detail later on.

Idea vs. Hypothesis Compared

You might be wondering whether ideas and hypotheses are the same thing. Well, there are a few distinctions.

An idea is simply a suggested proposal. Say, a teammate comes up with something you can bring to life during a brainstorming session or pitches in a suggestion like "How about we shorten the checkout process?". You can jot down such ideas and then consider working on them if they'll truly make a difference and improve the product, strategy, or result in other business benefits. Ideas may thus be used as the hypothesis foundation when you decide to prove a concept.

A hypothesis is the next step, when an idea gets wrapped with specifics to become an assumption that may be tested. As such, you can refine the idea by adding details to it. The previously mentioned idea can be worded into a product hypothesis statement like: "The cart abandonment rate is high, and many users flee at checkout. But if we shorten the checkout process by cutting down the number of steps to only two and get rid of four excessive fields, we'll simplify the user journey, boost satisfaction, and may get up to 15% more completed orders".

A hypothesis is something you can test in an attempt to reach a certain goal. Testing isn't obligatory in this scenario, of course, but the idea may be tested if you weigh the pros and cons and decide that the required effort is worth a try. We'll explain how to create hypothesis statements next.

How to Generate a Hypothesis for a Product

The last thing those developing a product want is to invest time and effort into something that won't bring any visible results, fall short of customer expectations, or won't live up to their needs. Therefore, to increase the chances of achieving a successful outcome and product-led growth , teams may need to revisit their product development approach by optimizing one of the starting points of the process: learning to make reasonable product hypotheses.

If the entire procedure is structured, this may assist you during such stages as the discovery phase and raise the odds of reaching your product goals and setting your business up for success. Yet, what's the entire process like?

• It all starts with identifying an existing problem . Is there a product area that's experiencing a downfall, a visible trend, or a market gap? Are users often complaining about something in their feedback? Or is there something you're willing to change (say, if you aim to get more profit, increase engagement, optimize a process, expand to a new market, or reach your OKRs and KPIs faster)?
• Teams then need to work on formulating a hypothesis . They put the statement into concise and short wording that describes what is expected to achieve. Importantly, it has to be relevant, actionable, backed by data, and without generalizations.
• Next, they have to test the hypothesis by running experiments to validate it (for instance, via A/B or multivariate testing, prototyping, feedback collection, or other ways).
• Then, the obtained results of the test must be analyzed . Did one element or page version outperform the other? Depending on what you're testing, you can look into various merits or product performance metrics (such as the click rate, bounce rate, or the number of sign-ups) to assess whether your prediction was correct.
• Finally, the teams can make conclusions that could lead to data-driven decisions. For example, they can make corresponding changes or roll back a step.

How Else Can You Generate Product Hypotheses?

Such processes imply sharing ideas when a problem is spotted by digging deep into facts and studying the possible risks, goals, benefits, and outcomes. You may apply various MVP tools like (FigJam, Notion, or Miro) that were designed to simplify brainstorming sessions, systemize pitched suggestions, and keep everyone organized without losing any ideas.

Predictive product analysis can also be integrated into this process, leveraging data and insights to anticipate market trends and consumer preferences, thus enhancing decision-making and product development strategies. This approach fosters a more proactive and informed approach to innovation, ensuring products are not only relevant but also resonate with the target audience, ultimately increasing their chances of success in the market.

Besides, you can settle on one of the many frameworks that facilitate decision-making processes , ideation phases, or feature prioritization . Such frameworks are best applicable if you need to test your assumptions and structure the validation process. These are a few common ones if you're looking toward a systematic approach:

• Business Model Canvas (used to establish the foundation of the business model and helps find answers to vitals like your value proposition, finding the right customer segment, or the ways to make revenue);
• Lean Startup framework (the lean startup framework uses a diagram-like format for capturing major processes and can be handy for testing various hypotheses like how much value a product brings or assumptions on personas, the problem, growth, etc.);
• Design Thinking Process (is all about interactive learning and involves getting an in-depth understanding of the customer needs and pain points, which can be formulated into hypotheses followed by simple prototypes and tests).

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How to Make a Hypothesis Statement for a Product

Once you've indicated the addressable problem or opportunity and broken down the issue in focus, you need to work on formulating the hypotheses and associated tasks. By the way, it works the same way if you want to prove that something will be false (a.k.a null hypothesis).

If you're unsure how to write a hypothesis statement, let's explore the essential steps that'll set you on the right track.

Step 1: Allocate the Variable Components

Product hypotheses are generally different for each case, so begin by pinpointing the major variables, i.e., the cause and effect . You'll need to outline what you think is supposed to happen if a change or action gets implemented.

Put simply, the "cause" is what you're planning to change, and the "effect" is what will indicate whether the change is bringing in the expected results. Falling back on the example we brought up earlier, the ineffective checkout process can be the cause, while the increased percentage of completed orders is the metric that'll show the effect.

Make sure to also note such vital points as:

• what the problem and solution are;
• what are the benefits or the expected impact/successful outcome;
• which user group is affected;
• what are the risks;
• what kind of experiments can help test the hypothesis;
• what can measure whether you were right or wrong.

Step 2: Ensure the Connection Is Specific and Logical

Mind that generic connections that lack specifics will get you nowhere. So if you're thinking about how to word a hypothesis statement, make sure that the cause and effect include clear reasons and a logical dependency .

Think about what can be the precise and link showing why A affects B. In our checkout example, it could be: fewer steps in the checkout and the removed excessive fields will speed up the process, help avoid confusion, irritate users less, and lead to more completed orders. That's much more explicit than just stating the fact that the checkout needs to be changed to get more completed orders.

Step 3: Decide on the Data You'll Collect

Certainly, multiple things can be used to measure the effect. Therefore, you need to choose the optimal metrics and validation criteria that'll best envision if you're moving in the right direction.

If you need a tip on how to create hypothesis statements that won't result in a waste of time, try to avoid vagueness and be as specific as you can when selecting what can best measure and assess the results of your hypothesis test. The criteria must be measurable and tied to the hypotheses . This can be a realistic percentage or number (say, you expect a 15% increase in completed orders or 2x fewer cart abandonment cases during the checkout phase).

Once again, if you're not realistic, then you might end up misinterpreting the results. Remember that sometimes an increase that's even as little as 2% can make a huge difference, so why make 50% the merit if it's not achievable in the first place?

Step 4: Settle on the Sequence

It's quite common that you'll end up with multiple product hypotheses. Some are more important than others, of course, and some will require more effort and input.

Therefore, just as with the features on your product development roadmap , prioritize your hypotheses according to their impact and importance. Then, group and order them, especially if the results of some hypotheses influence others on your list.

Product Hypothesis Examples

To demonstrate how to formulate your assumptions clearly, here are several more apart from the example of a hypothesis statement given above:

• Adding a wishlist feature to the cart with the possibility to send a gift hint to friends via email will increase the likelihood of making a sale and bring in additional sign-ups.
• Placing a limited-time promo code banner stripe on the home page will increase the number of sales in March.
• Moving up the call to action element on the landing page and changing the button text will increase the click-through rate twice.
• By highlighting a new way to use the product, we'll target a niche customer segment (i.e., single parents under 30) and acquire 5% more leads. 

How to Validate Hypothesis Statements: The Process Explained

There are multiple options when it comes to validating hypothesis statements. To get appropriate results, you have to come up with the right experiment that'll help you test the hypothesis. You'll need a control group or people who represent your target audience segments or groups to participate (otherwise, your results might not be accurate).

‍ What can serve as the experiment you may run? Experiments may take tons of different forms, and you'll need to choose the one that clicks best with your hypothesis goals (and your available resources, of course). The same goes for how long you'll have to carry out the test (say, a time period of two months or as little as two weeks). Here are several to get you started.

Feedback and User Testing

Talking to users, potential customers, or members of your own online startup community can be another way to test your hypotheses. You may use surveys, questionnaires, or opt for more extensive interviews to validate hypothesis statements and find out what people think. This assumption validation approach involves your existing or potential users and might require some additional time, but can bring you many insights.

Conduct A/B or Multivariate Tests

One of the experiments you may develop involves making more than one version of an element or page to see which option resonates with the users more. As such, you can have a call to action block with different wording or play around with the colors, imagery, visuals, and other things.

To run such split experiments, you can apply tools like VWO that allows to easily construct alternative designs and split what your users see (e.g., one half of the users will see version one, while the other half will see version two). You can track various metrics and apply heatmaps, click maps, and screen recordings to learn more about user response and behavior. Mind, though, that the key to such tests is to get as many users as you can give the tests time. Don't jump to conclusions too soon or if very few people participated in your experiment.

Build Prototypes and Fake Doors

Demos and clickable prototypes can be a great way to save time and money on costly feature or product development. A prototype also allows you to refine the design. However, they can also serve as experiments for validating hypotheses, collecting data, and getting feedback.

For instance, if you have a new feature in mind and want to ensure there is interest, you can utilize such MVP types as fake doors . Make a short demo recording of the feature and place it on your landing page to track interest or test how many people sign up.

Usability Testing

Similarly, you can run experiments to observe how users interact with the feature, page, product, etc. Usually, such experiments are held on prototype testing platforms with a focus group representing your target visitors. By showing a prototype or early version of the design to users, you can view how people use the solution, where they face problems, or what they don't understand. This may be very helpful if you have hypotheses regarding redesigns and user experience improvements before you move on from prototype to MVP development.

You can even take it a few steps further and build a barebone feature version that people can really interact with, yet you'll be the one behind the curtain to make it happen. There were many MVP examples when companies applied Wizard of Oz or concierge MVPs to validate their hypotheses.

Or you can actually develop some functionality but release it for only a limited number of people to see. This is referred to as a feature flag , which can show really specific results but is effort-intensive. 

What Comes After Hypothesis Validation?

Analysis is what you move on to once you've run the experiment. This is the time to review the collected data, metrics, and feedback to validate (or invalidate) the hypothesis.

You have to evaluate the experiment's results to determine whether your product hypotheses were valid or not. For example, if you were testing two versions of an element design, color scheme, or copy, look into which one performed best.

It is crucial to be certain that you have enough data to draw conclusions, though, and that it's accurate and unbiased . Because if you don't, this may be a sign that your experiment needs to be run for some additional time, be altered, or held once again. You won't want to make a solid decision based on uncertain or misleading results, right?

• If the hypothesis was supported , proceed to making corresponding changes (such as implementing a new feature, changing the design, rephrasing your copy, etc.). Remember that your aim was to learn and iterate to improve.
• If your hypothesis was proven false , think of it as a valuable learning experience. The main goal is to learn from the results and be able to adjust your processes accordingly. Dig deep to find out what went wrong, look for patterns and things that may have skewed the results. But if all signs show that you were wrong with your hypothesis, accept this outcome as a fact, and move on. This can help you make conclusions on how to better formulate your product hypotheses next time. Don't be too judgemental, though, as a failed experiment might only mean that you need to improve the current hypothesis, revise it, or create a new one based on the results of this experiment, and run the process once more.

On another note, make sure to record your hypotheses and experiment results . Some companies use CRMs to jot down the key findings, while others use something as simple as Google Docs. Either way, this can be your single source of truth that can help you avoid running the same experiments or allow you to compare results over time.

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Final Thoughts on Product Hypotheses

The hypothesis-driven approach in product development is a great way to avoid uncalled-for risks and pricey mistakes. You can back up your assumptions with facts, observe your target audience's reactions, and be more certain that this move will deliver value.

However, this only makes sense if the validation of hypothesis statements is backed by relevant data that'll allow you to determine whether the hypothesis is valid or not. By doing so, you can be certain that you're developing and testing hypotheses to accelerate your product management and avoiding decisions based on guesswork.

Certainly, a failed experiment may bring you just as much knowledge and findings as one that succeeds. Teams have to learn from their mistakes, boost their hypothesis generation and testing knowledge, and make improvements according to the results of their experiments. This is an ongoing process, of course, as no product can grow if it isn't iterated and improved.

If you're only planning to or are currently building a product, Upsilon can lend you a helping hand. Our team has years of experience providing product development services for growth-stage startups and building MVPs for early-stage businesses , so you can use our expertise and knowledge to dodge many mistakes. Don't be shy to contact us to discuss your needs! 

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How to Generate and Validate Product Hypotheses

What is a product hypothesis.

A hypothesis is a testable statement that predicts the relationship between two or more variables. In product development, we generate hypotheses to validate assumptions about customer behavior, market needs, or the potential impact of product changes. These experimental efforts help us refine the user experience and get closer to finding a product-market fit.

Product hypotheses are a key element of data-driven product development and decision-making. Testing them enables us to solve problems more efficiently and remove our own biases from the solutions we put forward.

Here’s an example: ‘If we improve the page load speed on our website (variable 1), then we will increase the number of signups by 15% (variable 2).’ So if we improve the page load speed, and the number of signups increases, then our hypothesis has been proven. If the number did not increase significantly (or not at all), then our hypothesis has been disproven.

In general, product managers are constantly creating and testing hypotheses. But in the context of new product development , hypothesis generation/testing occurs during the validation stage, right after idea screening .

Now before we go any further, let’s get one thing straight: What’s the difference between an idea and a hypothesis?

Idea vs hypothesis

Innovation expert Michael Schrage makes this distinction between hypotheses and ideas – unlike an idea, a hypothesis comes with built-in accountability. “But what’s the accountability for a good idea?” Schrage asks. “The fact that a lot of people think it’s a good idea? That’s a popularity contest.” So, not only should a hypothesis be tested, but by its very nature, it can be tested.

At Railsware, we’ve built our product development services on the careful selection, prioritization, and validation of ideas. Here’s how we distinguish between ideas and hypotheses:

Idea: A creative suggestion about how we might exploit a gap in the market, add value to an existing product, or bring attention to our product. Crucially, an idea is just a thought. It can form the basis of a hypothesis but it is not necessarily expected to be proven or disproven.

• We should get an interview with the CEO of our company published on TechCrunch.
• Why don’t we redesign our website?
• The Coupler.io team should create video tutorials on how to export data from different apps, and publish them on YouTube.
• Why not add a new ‘email templates’ feature to our Mailtrap product?

Hypothesis: A way of framing an idea or assumption so that it is testable, specific, and aligns with our wider product/team/organizational goals.

Examples: 

• If we add a new ‘email templates’ feature to Mailtrap, we’ll see an increase in active usage of our email-sending API.
• Creating relevant video tutorials and uploading them to YouTube will lead to an increase in Coupler.io signups.
• If we publish an interview with our CEO on TechCrunch, 500 people will visit our website and 10 of them will install our product.

Now, it’s worth mentioning that not all hypotheses require testing . Sometimes, the process of creating hypotheses is just an exercise in critical thinking. And the simple act of analyzing your statement tells whether you should run an experiment or not. Remember: testing isn’t mandatory, but your hypotheses should always be inherently testable.

Let’s consider the TechCrunch article example again. In that hypothesis, we expect 500 readers to visit our product website, and a 2% conversion rate of those unique visitors to product users i.e. 10 people. But is that marginal increase worth all the effort? Conducting an interview with our CEO, creating the content, and collaborating with the TechCrunch content team – all of these tasks take time (and money) to execute. And by formulating that hypothesis, we can clearly see that in this case, the drawbacks (efforts) outweigh the benefits. So, no need to test it.

In a similar vein, a hypothesis statement can be a tool to prioritize your activities based on impact. We typically use the following criteria:

• The quality of impact
• The size of the impact
• The probability of impact

This lets us organize our efforts according to their potential outcomes – not the coolness of the idea, its popularity among the team, etc.

Now that we’ve established what a product hypothesis is, let’s discuss how to create one.

Start with a problem statement

Before you jump into product hypothesis generation, we highly recommend formulating a problem statement. This is a short, concise description of the issue you are trying to solve. It helps teams stay on track as they formalize the hypothesis and design the product experiments. It can also be shared with stakeholders to ensure that everyone is on the same page.

The statement can be worded however you like, as long as it’s actionable, specific, and based on data-driven insights or research. It should clearly outline the problem or opportunity you want to address.

Here’s an example: Our bounce rate is high (more than 90%) and we are struggling to convert website visitors into actual users. How might we improve site performance to boost our conversion rate?

How to generate product hypotheses

Now let’s explore some common, everyday scenarios that lead to product hypothesis generation. For our teams here at Railsware, it’s when:

• There’s a problem with an unclear root cause e.g. a sudden drop in one part of the onboarding funnel. We identify these issues by checking our product metrics or reviewing customer complaints.
• We are running ideation sessions on how to reach our goals (increase MRR, increase the number of users invited to an account, etc.)
• We are exploring growth opportunities e.g. changing a pricing plan, making product improvements , breaking into a new market.
• We receive customer feedback. For example, some users have complained about difficulties setting up a workspace within the product. So, we build a hypothesis on how to help them with the setup.

BRIDGES framework for ideation

When we are tackling a complex problem or looking for ways to grow the product, our teams use BRIDGeS – a robust decision-making and ideation framework. BRIDGeS makes our product discovery sessions more efficient. It lets us dive deep into the context of our problem so that we can develop targeted solutions worthy of testing.

Between 2-8 stakeholders take part in a BRIDGeS session. The ideation sessions are usually led by a product manager and can include other subject matter experts such as developers, designers, data analysts, or marketing specialists. You can use a virtual whiteboard such as Figjam or Miro (see our Figma template ) to record each colored note.

In the first half of a BRIDGeS session, participants examine the Benefits, Risks, Issues, and Goals of their subject in the ‘Problem Space.’ A subject is anything that is being described or dealt with; for instance, Coupler.io’s growth opportunities. Benefits are the value that a future solution can bring, Risks are potential issues they might face, Issues are their existing problems, and Goals are what the subject hopes to gain from the future solution. Each descriptor should have a designated color.

After we have broken down the problem using each of these descriptors, we move into the Solution Space. This is where we develop solution variations based on all of the benefits/risks/issues identified in the Problem Space (see the Uber case study for an in-depth example).

In the Solution Space, we start prioritizing those solutions and deciding which ones are worthy of further exploration outside of the framework – via product hypothesis formulation and testing, for example. At the very least, after the session, we will have a list of epics and nested tasks ready to add to our product roadmap.

How to write a product hypothesis statement

Across organizations, product hypothesis statements might vary in their subject, tone, and precise wording. But some elements never change. As we mentioned earlier, a hypothesis statement must always have two or more variables and a connecting factor.

1. Identify variables

Since these components form the bulk of a hypothesis statement, let’s start with a brief definition.

First of all, variables in a hypothesis statement can be split into two camps: dependent and independent. Without getting too theoretical, we can describe the independent variable as the cause, and the dependent variable as the effect . So in the Mailtrap example we mentioned earlier, the ‘add email templates feature’ is the cause i.e. the element we want to manipulate. Meanwhile, ‘increased usage of email sending API’ is the effect i.e the element we will observe.

Independent variables can be any change you plan to make to your product. For example, tweaking some landing page copy, adding a chatbot to the homepage, or enhancing the search bar filter functionality.

Dependent variables are usually metrics. Here are a few that we often test in product development:

• Number of sign-ups
• Number of purchases
• Activation rate (activation signals differ from product to product)
• Number of specific plans purchased
• Feature usage (API activation, for example)
• Number of active users

Bear in mind that your concept or desired change can be measured with different metrics. Make sure that your variables are well-defined, and be deliberate in how you measure your concepts so that there’s no room for misinterpretation or ambiguity.

For example, in the hypothesis ‘Users drop off because they find it hard to set up a project’ variables are poorly defined. Phrases like ‘drop off’ and ‘hard to set up’ are too vague. A much better way of saying it would be: If project automation rules are pre-defined (email sequence to responsible, scheduled tickets creation), we’ll see a decrease in churn. In this example, it’s clear which dependent variable has been chosen and why.

And remember, when product managers focus on delighting users and building something of value, it’s easier to market and monetize it. That’s why at Railsware, our product hypotheses often focus on how to increase the usage of a feature or product. If users love our product(s) and know how to leverage its benefits, we can spend less time worrying about how to improve conversion rates or actively grow our revenue, and more time enhancing the user experience and nurturing our audience.

2. Make the connection

The relationship between variables should be clear and logical. If it’s not, then it doesn’t matter how well-chosen your variables are – your test results won’t be reliable.

To demonstrate this point, let’s explore a previous example again: page load speed and signups.

Through prior research, you might already know that conversion rates are 3x higher for sites that load in 1 second compared to sites that take 5 seconds to load. Since there appears to be a strong connection between load speed and signups in general, you might want to see if this is also true for your product.

Here are some common pitfalls to avoid when defining the relationship between two or more variables:

Relationship is weak. Let’s say you hypothesize that an increase in website traffic will lead to an increase in sign-ups. This is a weak connection since website visitors aren’t necessarily motivated to use your product; there are more steps involved. A better example is ‘If we change the CTA on the pricing page, then the number of signups will increase.’ This connection is much stronger and more direct.

Relationship is far-fetched. This often happens when one of the variables is founded on a vanity metric. For example, increasing the number of social media subscribers will lead to an increase in sign-ups. However, there’s no particular reason why a social media follower would be interested in using your product. Oftentimes, it’s simply your social media content that appeals to them (and your audience isn’t interested in a product).

Variables are co-dependent. Variables should always be isolated from one another. Let’s say we removed the option “Register with Google” from our app. In this case, we can expect fewer users with Google workspace accounts to register. Obviously, it’s because there’s a direct dependency between variables (no registration with Google→no users with Google workspace accounts).

3. Set validation criteria

First, build some confirmation criteria into your statement . Think in terms of percentages (e.g. increase/decrease by 5%) and choose a relevant product metric to track e.g. activation rate if your hypothesis relates to onboarding. Consider that you don’t always have to hit the bullseye for your hypothesis to be considered valid. Perhaps a 3% increase is just as acceptable as a 5% one. And it still proves that a connection between your variables exists.

Secondly, you should also make sure that your hypothesis statement is realistic . Let’s say you have a hypothesis that ‘If we show users a banner with our new feature, then feature usage will increase by 10%.’ A few questions to ask yourself are: Is 10% a reasonable increase, based on your current feature usage data? Do you have the resources to create the tests (experimenting with multiple variations, distributing on different channels: in-app, emails, blog posts)?

Null hypothesis and alternative hypothesis

In statistical research, there are two ways of stating a hypothesis: null or alternative. But this scientific method has its place in hypothesis-driven development too…

Alternative hypothesis: A statement that you intend to prove as being true by running an experiment and analyzing the results. Hint: it’s the same as the other hypothesis examples we’ve described so far.

Example: If we change the landing page copy, then the number of signups will increase.

Null hypothesis: A statement you want to disprove by running an experiment and analyzing the results. It predicts that your new feature or change to the user experience will not have the desired effect.

Example: The number of signups will not increase if we make a change to the landing page copy.

What’s the point? Well, let’s consider the phrase ‘innocent until proven guilty’ as a version of a null hypothesis. We don’t assume that there is any relationship between the ‘defendant’ and the ‘crime’ until we have proof. So, we run a test, gather data, and analyze our findings — which gives us enough proof to reject the null hypothesis and validate the alternative. All of this helps us to have more confidence in our results.

Now that you have generated your hypotheses, and created statements, it’s time to prepare your list for testing.

Prioritizing hypotheses for testing

Not all hypotheses are created equal. Some will be essential to your immediate goal of growing the product e.g. adding a new data destination for Coupler.io. Others will be based on nice-to-haves or small fixes e.g. updating graphics on the website homepage.

Prioritization helps us focus on the most impactful solutions as we are building a product roadmap or narrowing down the backlog . To determine which hypotheses are the most critical, we use the MoSCoW framework. It allows us to assign a level of urgency and importance to each product hypothesis so we can filter the best 3-5 for testing.

MoSCoW is an acronym for Must-have, Should-have, Could-have, and Won’t-have. Here’s a breakdown:

• Must-have – hypotheses that must be tested, because they are strongly linked to our immediate project goals.
• Should-have – hypotheses that are closely related to our immediate project goals, but aren’t the top priority.
• Could-have – hypotheses of nice-to-haves that can wait until later for testing. 
• Won’t-have – low-priority hypotheses that we may or may not test later on when we have more time.

How to test product hypotheses

Once you have selected a hypothesis, it’s time to test it. This will involve running one or more product experiments in order to check the validity of your claim.

The tricky part is deciding what type of experiment to run, and how many. Ultimately, this all depends on the subject of your hypothesis – whether it’s a simple copy change or a whole new feature. For instance, it’s not necessary to create a clickable prototype for a landing page redesign. In that case, a user-wide update would do.

On that note, here are some of the approaches we take to hypothesis testing at Railsware:

A/B testing

A/B or split testing involves creating two or more different versions of a webpage/feature/functionality and collecting information about how users respond to them.

Let’s say you wanted to validate a hypothesis about the placement of a search bar on your application homepage. You could design an A/B test that shows two different versions of that search bar’s placement to your users (who have been split equally into two camps: a control group and a variant group). Then, you would choose the best option based on user data. A/B tests are suitable for testing responses to user experience changes, especially if you have more than one solution to test.

Prototyping

When it comes to testing a new product design, prototyping is the method of choice for many Lean startups and organizations. It’s a cost-effective way of collecting feedback from users, fast, and it’s possible to create prototypes of individual features too. You may take this approach to hypothesis testing if you are working on rolling out a significant new change e.g adding a brand-new feature, redesigning some aspect of the user flow, etc. To control costs at this point in the new product development process , choose the right tools — think Figma for clickable walkthroughs or no-code platforms like Bubble.

Deliveroo feature prototype example

Let’s look at how feature prototyping worked for the food delivery app, Deliveroo, when their product team wanted to ‘explore personalized recommendations, better filtering and improved search’ in 2018. To begin, they created a prototype of the customer discovery feature using web design application, Framer.

One of the most important aspects of this feature prototype was that it contained live data — real restaurants, real locations. For test users, this made the hypothetical feature feel more authentic. They were seeing listings and recommendations for real restaurants in their area, which helped immerse them in the user experience, and generate more honest and specific feedback. Deliveroo was then able to implement this feedback in subsequent iterations.

Asking your users

Interviewing customers is an excellent way to validate product hypotheses. It’s a form of qualitative testing that, in our experience, produces better insights than user surveys or general user research. Sessions are typically run by product managers and involve asking  in-depth interview questions  to one customer at a time. They can be conducted in person or online (through a virtual call center , for instance) and last anywhere between 30 minutes to 1 hour.

Although CustDev interviews may require more effort to execute than other tests (the process of finding participants, devising questions, organizing interviews, and honing interview skills can be time-consuming), it’s still a highly rewarding approach. You can quickly validate assumptions by asking customers about their pain points, concerns, habits, processes they follow, and analyzing how your solution fits into all of that.

Wizard of Oz

The Wizard of Oz approach is suitable for gauging user interest in new features or functionalities. It’s done by creating a prototype of a fake or future feature and monitoring how your customers or test users interact with it.

For example, you might have a hypothesis that your number of active users will increase by 15% if you introduce a new feature. So, you design a new bare-bones page or simple button that invites users to access it. But when they click on the button, a pop-up appears with a message such as ‘coming soon.’

By measuring the frequency of those clicks, you could learn a lot about the demand for this new feature/functionality. However, while these tests can deliver fast results, they carry the risk of backfiring. Some customers may find fake features misleading, making them less likely to engage with your product in the future.

User-wide updates

One of the speediest ways to test your hypothesis is by rolling out an update for all users. It can take less time and effort to set up than other tests (depending on how big of an update it is). But due to the risk involved, you should stick to only performing these kinds of tests on small-scale hypotheses. Our teams only take this approach when we are almost certain that our hypothesis is valid.

For example, we once had an assumption that the name of one of Mailtrap ’s entities was the root cause of a low activation rate. Being an active Mailtrap customer meant that you were regularly sending test emails to a place called ‘Demo Inbox.’ We hypothesized that the name was confusing (the word ‘demo’ implied it was not the main inbox) and this was preventing new users from engaging with their accounts. So, we updated the page, changed the name to ‘My Inbox’ and added some ‘to-do’ steps for new users. We saw an increase in our activation rate almost immediately, validating our hypothesis.

Feature flags

Creating feature flags involves only releasing a new feature to a particular subset or small percentage of users. These features come with a built-in kill switch; a piece of code that can be executed or skipped, depending on who’s interacting with your product.

Since you are only showing this new feature to a selected group, feature flags are an especially low-risk method of testing your product hypothesis (compared to Wizard of Oz, for example, where you have much less control). However, they are also a little bit more complex to execute than the others — you will need to have an actual coded product for starters, as well as some technical knowledge, in order to add the modifiers ( only when… ) to your new coded feature.

Let’s revisit the landing page copy example again, this time in the context of testing.

So, for the hypothesis ‘If we change the landing page copy, then the number of signups will increase,’ there are several options for experimentation. We could share the copy with a small sample of our users, or even release a user-wide update. But A/B testing is probably the best fit for this task. Depending on our budget and goal, we could test several different pieces of copy, such as:

• The current landing page copy
• Copy that we paid a marketing agency 10 grand for
• Generic copy we wrote ourselves, or removing most of the original copy – just to see how making even a small change might affect our numbers.

Remember, every hypothesis test must have a reasonable endpoint. The exact length of the test will depend on the type of feature/functionality you are testing, the size of your user base, and how much data you need to gather. Just make sure that the experiment running time matches the hypothesis scope. For instance, there is no need to spend 8 weeks experimenting with a piece of landing page copy. That timeline is more appropriate for say, a Wizard of Oz feature.

Recording hypotheses statements and test results

Finally, it’s time to talk about where you will write down and keep track of your hypotheses. Creating a single source of truth will enable you to track all aspects of hypothesis generation and testing with ease.

At Railsware, our product managers create a document for each individual hypothesis, using tools such as Coda or Google Sheets. In that document, we record the hypothesis statement, as well as our plans, process, results, screenshots, product metrics, and assumptions.

We share this document with our team and stakeholders, to ensure transparency and invite feedback. It’s also a resource we can refer back to when we are discussing a new hypothesis — a place where we can quickly access information relating to a previous test.

Understanding test results and taking action

The other half of validating product hypotheses involves evaluating data and drawing reasonable conclusions based on what you find. We do so by analyzing our chosen product metric(s) and deciding whether there is enough data available to make a solid decision. If not, we may extend the test’s duration or run another one. Otherwise, we move forward. An experimental feature becomes a real feature, a chatbot gets implemented on the customer support page, and so on.

Something to keep in mind: the integrity of your data is tied to how well the test was executed, so here are a few points to consider when you are testing and analyzing results:

Gather and analyze data carefully. Ensure that your data is clean and up-to-date when running quantitative tests and tracking responses via analytics dashboards. If you are doing customer interviews, make sure to record the meetings (with consent) so that your notes will be as accurate as possible.

Conduct the right amount of product experiments. It can take more than one test to determine whether your hypothesis is valid or invalid. However, don’t waste too much time experimenting in the hopes of getting the result you want. Know when to accept the evidence and move on.

Choose the right audience segment. Don’t cast your net too wide. Be specific about who you want to collect data from prior to running the test. Otherwise, your test results will be misleading and you won’t learn anything new.

Watch out for bias. Avoid confirmation bias at all costs. Don’t make the mistake of including irrelevant data just because it bolsters your results. For example, if you are gathering data about how users are interacting with your product Monday-Friday, don’t include weekend data just because doing so would alter the data and ‘validate’ your hypothesis.

• Not all failed hypotheses should be treated as losses. Even if you didn’t get the outcome you were hoping for, you may still have improved your product. Let’s say you implemented SSO authentication for premium users, but unfortunately, your free users didn’t end up switching to premium plans. In this case, you still added value to the product by streamlining the login process for paying users.
• Yes, taking a hypothesis-driven approach to product development is important. But remember, you don’t have to test everything . Use common sense first. For example, if your website copy is confusing and doesn’t portray the value of the product, then you should still strive to replace it with better copy – regardless of how this affects your numbers in the short term.

Wrapping Up

The process of generating and validating product hypotheses is actually pretty straightforward once you’ve got the hang of it. All you need is a valid question or problem, a testable statement, and a method of validation. Sure, hypothesis-driven development requires more of a time commitment than just ‘giving it a go.’ But ultimately, it will help you tune the product to the wants and needs of your customers.

If you share our data-driven approach to product development and engineering, check out our services page to learn more about how we work with our clients!

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Product best practices

Product hypothesis - a guide to create meaningful hypotheses.

13 December, 2023

Growth Manager

Data-driven development is no different than a scientific experiment. You repeatedly form hypotheses, test them, and either implement (or reject) them based on the results. It’s a proven system that leads to better apps and happier users.

Let’s get started.

What is a product hypothesis?

A product hypothesis is an educated guess about how a change to a product will impact important metrics like revenue or user engagement. It's a testable statement that needs to be validated to determine its accuracy.

The most common format for product hypotheses is “If… than…”:

“If we increase the font size on our homepage, then more customers will convert.”

“If we reduce form fields from 5 to 3, then more users will complete the signup process.”

At UXCam, we believe in a data-driven approach to developing product features. Hypotheses provide an effective way to structure development and measure results so you can make informed decisions about how your product evolves over time.

Take PlaceMakers , for example.

PlaceMakers faced challenges with their app during the COVID-19 pandemic. Due to supply chain shortages, stock levels were not being updated in real-time, causing customers to add unavailable products to their baskets. The team added a “Constrained Product” label, but this caused sales to plummet.

The team then turned to UXCam’s session replays and heatmaps to investigate, and hypothesized that their messaging for constrained products was too strong. The team redesigned the messaging with a more positive approach, and sales didn’t just recover—they doubled.

Types of product hypothesis

1. counter-hypothesis.

A counter-hypothesis is an alternative proposition that challenges the initial hypothesis. It’s used to test the robustness of the original hypothesis and make sure that the product development process considers all possible scenarios. 

For instance, if the original hypothesis is “Reducing the sign-up steps from 3 to 1 will increase sign-ups by 25% for new visitors after 1,000 visits to the sign-up page,” a counter-hypothesis could be “Reducing the sign-up steps will not significantly affect the sign-up rate.

2. Alternative hypothesis

An alternative hypothesis predicts an effect in the population. It’s the opposite of the null hypothesis, which states there’s no effect. 

For example, if the null hypothesis is “improving the page load speed on our mobile app will not affect the number of sign-ups,” the alternative hypothesis could be “improving the page load speed on our mobile app will increase the number of sign-ups by 15%.”

3. Second-order hypothesis

Second-order hypotheses are derived from the initial hypothesis and provide more specific predictions. 

For instance, “if the initial hypothesis is Improving the page load speed on our mobile app will increase the number of sign-ups,” a second-order hypothesis could be “Improving the page load speed on our mobile app will increase the number of sign-ups.”

Why is a product hypothesis important?

Guided product development.

A product hypothesis serves as a guiding light in the product development process. In the case of PlaceMakers, the product owner’s hypothesis that users would benefit from knowing the availability of items upfront before adding them to the basket helped their team focus on the most critical aspects of the product. It ensured that their efforts were directed towards features and improvements that have the potential to deliver the most value. 

Improved efficiency

Product hypotheses enable teams to solve problems more efficiently and remove biases from the solutions they put forward. By testing the hypothesis, PlaceMakers aimed to improve efficiency by addressing the issue of stock levels not being updated in real-time and customers adding unavailable products to their baskets.

Risk mitigation

By validating assumptions before building the product, teams can significantly reduce the risk of failure. This is particularly important in today’s fast-paced, highly competitive business environment, where the cost of failure can be high.

Validating assumptions through the hypothesis helped mitigate the risk of failure for PlaceMakers, as they were able to identify and solve the issue within a three-day period.

Data-driven decision-making

Product hypotheses are a key element of data-driven product development and decision-making. They provide a solid foundation for making informed, data-driven decisions, which can lead to more effective and successful product development strategies. 

The use of UXCam's Session Replay and Heatmaps features provided valuable data for data-driven decision-making, allowing PlaceMakers to quickly identify the problem and revise their messaging approach, leading to a doubling of sales.

How to create a great product hypothesis

Map important user flows

Identify any bottlenecks

Look for interesting behavior patterns

Turn patterns into hypotheses

Step 1 - Map important user flows

A good product hypothesis starts with an understanding of how users more around your product—what paths they take, what features they use, how often they return, etc. Before you can begin hypothesizing, it’s important to map out key user flows and journey maps that will help inform your hypothesis.

To do that, you’ll need to use a monitoring tool like UXCam .

UXCam integrates with your app through a lightweight SDK and automatically tracks every user interaction using tagless autocapture. That leads to tons of data on user behavior that you can use to form hypotheses.

At this stage, there are two specific visualizations that are especially helpful:

Funnels : Funnels are great for identifying drop off points and understanding which steps in a process, transition or journey lead to success.

In other words, you’re using these two tools to define key in-app flows and to measure the effectiveness of these flows (in that order).

Average time to conversion in highlights bar.

Step 2 - Identify any bottlenecks

Once you’ve set up monitoring and have started collecting data, you’ll start looking for bottlenecks—points along a key app flow that are tripping users up. At every stage in a funnel, there’s going to be dropoffs, but too many dropoffs can be a sign of a problem.

UXCam makes it easy to spot dropoffs by displaying them visually in every funnel. While there’s no benchmark for when you should be concerned, anything above a 10% dropoff could mean that further investigation is needed.

How do you investigate? By zooming in.

Step 3 - Look for interesting behavior patterns

At this stage, you’ve noticed a concerning trend and are zooming in on individual user experiences to humanize the trend and add important context.

The best way to do this is with session replay tools and event analytics. With a tool like UXCam, you can segment app data to isolate sessions that fit the trend. You can then investigate real user sessions by watching videos of their experience or by looking into their event logs. This helps you see exactly what caused the behavior you’re investigating.

For example, let’s say you notice that 20% of users who add an item to their cart leave the app about 5 minutes later. You can use session replay to look for the behavioral patterns that lead up to users leaving—such as how long they linger on a certain page or if they get stuck in the checkout process.

Step 4 - Turn patterns into hypotheses

Once you’ve checked out a number of user sessions, you can start to craft a product hypothesis.

This usually takes the form of an “If… then…” statement, like:

“If we optimize the checkout process for mobile users, then more customers will complete their purchase.”

These hypotheses can be tested using A/B testing and other user research tools to help you understand if your changes are having an impact on user behavior.

Product hypothesis emphasizes the importance of formulating clear and testable hypotheses when developing a product. It highlights that a well-defined hypothesis can guide the product development process, align stakeholders, and minimize uncertainty.

UXCam arms product teams with all the tools they need to form meaningful hypotheses that drive development in a positive direction. Put your app’s data to work and start optimizing today— sign up for a free account .

You might also be interested in these;

Product experimentation framework for mobile product teams

7 Best AB testing tools for mobile apps

A practical guide to product experimentation

5 Best product experimentation tools & software

How to use data to challenge the HiPPO

Ardent technophile exploring the world of mobile app product management at UXCam.

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4 types of product assumptions and how to test them

Understanding, identifying, and testing product assumptions is a cornerstone of product development.

To some extent, it’s the primary responsibility of a product manager to handle assumptions well to drive product outcomes.

Let’s dive deep into what assumptions are, why they are critical, the common types of assumptions, and, most importantly, how to test them.

What are product assumptions?

Product assumptions are preconceived beliefs or hypotheses that product managers establish during the product development cycle, providing an initial framework for decision-making. These assumptions, which can involve features, user behaviors, market trends, or technical feasibility, are integral to the iterative process of product creation and validation.

Assumptions guide the prototyping, testing, and adjustment stages, allowing the team to refine and improve the product in response to real-world feedback.

Leveraging product assumptions effectively is a cornerstone of risk management in product development because it aids in reducing uncertainty, saving resources, and accelerating time to market. Remember, a key part of a product manager’s role is to continuously challenge and validate product assumptions to ensure the product remains aligned with consumer needs and market dynamics.

Whatever you do, you don’t do it without a reason. For example, if you are building a retention-focused feature to drive revenue, you automatically assume that the feature will improve your revenue metrics and that it’ll deliver enough value for users that they’ll retain better.

In short, assumptions are all the beliefs you have when pursuing a particular idea, whether validated or not.

Why are assumptions important for product managers?

You can’t overemphasize the importance of assumptions in product management. For PMs, they are the building block of everything we do.

Ultimately, our job is to drive product outcomes by pursuing various initiatives we believe will contribute to the outcome. We decide which initiatives to pursue based on the beliefs we hold:

If our assumptions are correct, the initiative is a success, and there should be a tangible impact on the outcome. If they turn out wrong, we might fail to drive the impact we hope to see. We may even do more harm than good.

Because one initiative is often based on numerous assumptions, and various solutions can share the same assumptions, testing individual hypotheses is faster and cheaper than testing whole initiatives:

Moreover, testing an initiative with multiple unvalidated assumptions makes it hard to distinguish which hypotheses contributed to its success and which didn’t. Testing shared assumptions can help us raise confidence in multiple solutions simultaneously.

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In most cases, you’re better off focusing on testing individual assumptions first than jumping straight into solution development.

4 types of product assumptions

There are various types of assumptions. However, as a product manager, there are four important assumptions that you must understand and learn how to test:

• Desirability assumptions
• Viability assumptions
• Feasibility assumptions
• Usability assumptions

1. Desirability assumptions

When you assume solution desirability, you are trying to answer the question, “Do our users want this solution?”

After all, in the vast majority of cases, there’s no reason to pursue an initiative that isn’t interesting for your end-users.

Desirability assumptions include questions such as:

• Does this problem solve a painful enough problem?
• Is the problem we are solving relevant to enough users?
• Is our proposed way of solving the problem optimal?
• Will users understand the value they can get from this solution?

2. Viability assumptions

Viability determines whether the initiative makes sense from a business perspective.

Delivering value for users is great, but to be truly successful, an initiative must also deliver enough ROI for the business to grow and prosper. Of course, you might work for an NGO that doesn’t care about the revenue.

Viability assumptions include questions such as:

• Will we see a positive impact on business metrics?
• Does this initiative fit our current business model?
• Does the solution align with our long-term product strategy?
• Can we expect a satisfactory return on investment?

3. Feasibility assumptions

Even the most desirable and viable solutions are only relevant if they are possible to build, implement, and maintain.

Before committing to any direction, ensure you can deliver the initiative within your current constraints.

You can assess feasibility by answering questions such as:

• Does our current technology stack allow such an implementation?
• Do we have the resources and skillset to proceed with this initiative?
• Do we have means of maintaining the initiative?
• Can we handle the technical complexity of this solution?

4. Usability assumptions

Even after you implement a desirable, viable, and feasible solution, it won’t drive the expected results if users don’t understand how to use it.

The more usable the solution is, the more optimal outcomes it’ll yield.

Focus on answering questions such as:

• Are our users aware that the new solution exists?
• Do they understand what value they can get from it?
• Is it clear how to find and use the solution?
• Is there friction or needless complexity that might prevent users from adopting the solution?

How to use an assumption map

An assumption map is a powerful technique that can help you identify, organize, and prioritize assumptions you make with your initiatives.

Check out our assumption mapping article for more details if that sounds valuable.

For the purpose of this article, I’ll assume you’ve already identified and prioritized your assumptions.

Testing product assumptions

Now let’s take a look at some ways you can test your assumptions. While the best method depends heavily on the type of assumption you are testing, this library should be a solid starting point:

Testing desirability

Testing viability, testing feasibility, testing usability.

There’s no way to test desirability without interacting with your users. Get out of the door, one way or another, and see if the solution is something your users truly want.

Techniques for assessing the desirability of a solution include:

Landing pages

Crowdfunding, alpha and beta testing.

One of the fastest and most insightful desirability validation techniques is to interview your target users .

You don’t want to ask users upfront because doing so produces skewed answers. Instead, you want to understand the user’s problem, how they describe it, and the most significant pain points they have. You can then look at your proposed solution and judge whether it could potentially solve the problems users mentioned.

You can create a product landing page even if you don’t yet have the product. By monitoring the engagement on the site, you can gauge the overall interest in the solution; if users bounce from the site after a few seconds, they are probably not interested.

You can take it a step further and include the option to subscribe to a waitlist. Signing up would be a powerful signal that users are genuinely interested.

If you are building a B2B solution, you can try to actually sell it to potential clients. There are three ways to approach this:

• Mock sales — A sales simulation when you try to sell the solution but don’t commit to an actual sale
• Letter of intent — You ask your potential client to sign a letter of intent to buy the solution once it’s live
• Actual sale — In some cases, you might be able to finalize the sale before the product is even live, with an option to revert the sale if you decide not to pursue the direction after all

If people are willing to pay for the solution before it is even created, the desirability is really high.

Crowdfunding is a presale option for mass B2C consumers. However, it’s viable mostly for brand-new products.

By promoting your idea on sites like Kickstarter, you can not only gauge overall desirability but also capture funding to improve the viability of the idea.

The most powerful yet expensive way of testing desirability is to build a minimal version of the solution. You can then conduct alpha and beta tests to see actual user engagement and gather real-time feedback on the further direction.

Due to the cost, this method is recommended after you have some initial confirmation with other validation techniques.

You can test the viability of assumptions by taking a closer look at the business side of things to evaluate whether the initiative fits well or contradicts with other areas.

Techniques for testing the viability of your product include:

Business model review

Strategy canvas, business case.

The first step in assessing initiative viability is to review your current business model and see how it would fit there:

Does the solution connect well to your current value proposition and distribution channel? Do you have key resources and partners to pull it off? Does it sync well with key activities you are performing?

Ideally, your initiative will not only not disrupt your business model but also contribute to it as a whole.

A viable solution helps you build a competitive advantage in the market. One way to evaluate viability is to map a strategy canvas of your competitive alternatives and judge whether the initiative will help you strengthen your advantage or reduce your weaknesses:

A great solution helps you maintain and expand your competitive edge on the market.

With basic viability tested, it’s worth investing some time to build a robust business case.

Gather all relevant input and try to build well-informed projections:

• How many people can you reach?
• How expensive the solution is going to be?
• What’s the expected long-term revenue gain and maintenance cost?
• What is the anticipated ROI over time?

A strong business case will also help you pitch the idea to key stakeholders and compare the business viability of various initiatives and solutions to choose the most impactful one.

Validating whether a solution is possible to implement usually requires a team of subject matter experts to do a deep dive into potential implementation details. Two common approaches are

Technical research

Proof of concept (poc).

This step includes researching various implementation methods and limitations to determine whether a solution is feasible.

For example, suppose you are considering a range of trial lengths for various user segments in your mobile product. In that case, you might need to review app store policy and limitations to see if it’s allowed out of the box or if any external solution is necessary.

If an external solution is needed, you might investigate whether there’s an SDK supporting that or it requires building from scratch (thus increasing complexity and reducing the viability of the solution).

For more complex initiatives, you might need to develop a proof ofconcept. One could call it a “technical MVP”. It includes building the minimal version of the most uncertain part of the solution and evaluating if it even works. Proof of concept might vary from a few lines of code for simple tests to a fully-fledged development for the most complex initiatives.

Usability is the most straightforward thing to test. You want to put the solution in front of the user to see if they understand how to use it and what potential friction points are.

There are two common ways to do this:

Analytics review

Prototypes are at the forefront of usability testing. Build a simulation of the experience you want to provide, ask the user to finish a specific task, and observe how they interact with the product.

Depending on the level of uncertainty and the investment you want to make, prototypes can vary from quick-and-dirty paper prototypes to fully interactive, no-code solutions.

If you are already at an MVP stage, you have the benefit of having actual data on how the solution is used. Analyze this data closely to evaluate how discoverable the product is, how much time it takes for users to complete specific tasks, and what are the most common dropout moments.

Combining quantitative data review with qualitative insights from prototypes will help you validate most of your usability assumptions.

Every initiative you pursue is based on a set of underlying assumptions — that is, a set of preconceived beliefs we have when deciding which direction to pursue.

Validating these beliefs is a critical part of product management. After all, it’s easier and cheaper to test individual assumptions than to test solutions as a whole.

Make sure you identify your main desirability, viability, feasibility, and usability assumptions and test them before committing to a fully-fledged solution.

I recommend you store the insights from assumptions tests for future reference. Many solutions tend to share similar assumptions, so the insights might help you speed up your validation process in the future.

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Shipping Your Product in Iterations: A Guide to Hypothesis Testing

Glancing at the App Store on any phone will reveal that most installed apps have had updates released within the last week. Software products today are shipped in iterations to validate assumptions and hypotheses about what makes the product experience better for users.

By Kumara Raghavendra

Kumara has successfully delivered high-impact products in various industries ranging from eCommerce, healthcare, travel, and ride-hailing.

PREVIOUSLY AT

A look at the App Store on any phone will reveal that most installed apps have had updates released within the last week. A website visit after a few weeks might show some changes in the layout, user experience, or copy.

Today, software is shipped in iterations to validate assumptions and the product hypothesis about what makes a better user experience. At any given time, companies like booking.com (where I worked before) run hundreds of A/B tests on their sites for this very purpose.

For applications delivered over the internet, there is no need to decide on the look of a product 12-18 months in advance, and then build and eventually ship it. Instead, it is perfectly practical to release small changes that deliver value to users as they are being implemented, removing the need to make assumptions about user preferences and ideal solutions—for every assumption and hypothesis can be validated by designing a test to isolate the effect of each change.

In addition to delivering continuous value through improvements, this approach allows a product team to gather continuous feedback from users and then course-correct as needed. Creating and testing hypotheses every couple of weeks is a cheaper and easier way to build a course-correcting and iterative approach to creating product value .

What Is Hypothesis Testing in Product Management?

While shipping a feature to users, it is imperative to validate assumptions about design and features in order to understand their impact in the real world.

This validation is traditionally done through product hypothesis testing , during which the experimenter outlines a hypothesis for a change and then defines success. For instance, if a data product manager at Amazon has a hypothesis that showing bigger product images will raise conversion rates, then success is defined by higher conversion rates.

One of the key aspects of hypothesis testing is the isolation of different variables in the product experience in order to be able to attribute success (or failure) to the changes made. So, if our Amazon product manager had a further hypothesis that showing customer reviews right next to product images would improve conversion, it would not be possible to test both hypotheses at the same time. Doing so would result in failure to properly attribute causes and effects; therefore, the two changes must be isolated and tested individually.

Thus, product decisions on features should be backed by hypothesis testing to validate the performance of features.

Different Types of Hypothesis Testing

A/b testing.

One of the most common use cases to achieve hypothesis validation is randomized A/B testing, in which a change or feature is released at random to one-half of users (A) and withheld from the other half (B). Returning to the hypothesis of bigger product images improving conversion on Amazon, one-half of users will be shown the change, while the other half will see the website as it was before. The conversion will then be measured for each group (A and B) and compared. In case of a significant uplift in conversion for the group shown bigger product images, the conclusion would be that the original hypothesis was correct, and the change can be rolled out to all users.

Multivariate Testing

Ideally, each variable should be isolated and tested separately so as to conclusively attribute changes. However, such a sequential approach to testing can be very slow, especially when there are several versions to test. To continue with the example, in the hypothesis that bigger product images lead to higher conversion rates on Amazon, “bigger” is subjective, and several versions of “bigger” (e.g., 1.1x, 1.3x, and 1.5x) might need to be tested.

Instead of testing such cases sequentially, a multivariate test can be adopted, in which users are not split in half but into multiple variants. For instance, four groups (A, B, C, D) are made up of 25% of users each, where A-group users will not see any change, whereas those in variants B, C, and D will see images bigger by 1.1x, 1.3x, and 1.5x, respectively. In this test, multiple variants are simultaneously tested against the current version of the product in order to identify the best variant.

Before/After Testing

Sometimes, it is not possible to split the users in half (or into multiple variants) as there might be network effects in place. For example, if the test involves determining whether one logic for formulating surge prices on Uber is better than another, the drivers cannot be divided into different variants, as the logic takes into account the demand and supply mismatch of the entire city. In such cases, a test will have to compare the effects before the change and after the change in order to arrive at a conclusion.

However, the constraint here is the inability to isolate the effects of seasonality and externality that can differently affect the test and control periods. Suppose a change to the logic that determines surge pricing on Uber is made at time t , such that logic A is used before and logic B is used after. While the effects before and after time t can be compared, there is no guarantee that the effects are solely due to the change in logic. There could have been a difference in demand or other factors between the two time periods that resulted in a difference between the two.

Time-based On/Off Testing

The downsides of before/after testing can be overcome to a large extent by deploying time-based on/off testing, in which the change is introduced to all users for a certain period of time, turned off for an equal period of time, and then repeated for a longer duration.

For example, in the Uber use case, the change can be shown to drivers on Monday, withdrawn on Tuesday, shown again on Wednesday, and so on.

While this method doesn’t fully remove the effects of seasonality and externality, it does reduce them significantly, making such tests more robust.

Test Design

Choosing the right test for the use case at hand is an essential step in validating a hypothesis in the quickest and most robust way. Once the choice is made, the details of the test design can be outlined.

The test design is simply a coherent outline of:

• The hypothesis to be tested: Showing users bigger product images will lead them to purchase more products.
• Success metrics for the test: Customer conversion
• Decision-making criteria for the test: The test validates the hypothesis that users in the variant show a higher conversion rate than those in the control group.
• Metrics that need to be instrumented to learn from the test: Customer conversion, clicks on product images

In the case of the product hypothesis example that bigger product images will lead to improved conversion on Amazon, the success metric is conversion and the decision criteria is an improvement in conversion.

After the right test is chosen and designed, and the success criteria and metrics are identified, the results must be analyzed. To do that, some statistical concepts are necessary.

When running tests, it is important to ensure that the two variants picked for the test (A and B) do not have a bias with respect to the success metric. For instance, if the variant that sees the bigger images already has a higher conversion than the variant that doesn’t see the change, then the test is biased and can lead to wrong conclusions.

In order to ensure no bias in sampling, one can observe the mean and variance for the success metric before the change is introduced.

Significance and Power

Once a difference between the two variants is observed, it is important to conclude that the change observed is an actual effect and not a random one. This can be done by computing the significance of the change in the success metric.

In layman’s terms, significance measures the frequency with which the test shows that bigger images lead to higher conversion when they actually don’t. Power measures the frequency with which the test tells us that bigger images lead to higher conversion when they actually do.

So, tests need to have a high value of power and a low value of significance for more accurate results.

While an in-depth exploration of the statistical concepts involved in product management hypothesis testing is out of scope here, the following actions are recommended to enhance knowledge on this front:

• Data analysts and data engineers are usually adept at identifying the right test designs and can guide product managers, so make sure to utilize their expertise early in the process.
• There are numerous online courses on hypothesis testing, A/B testing, and related statistical concepts, such as Udemy , Udacity , and Coursera .
• Using tools such as Google’s Firebase and Optimizely can make the process easier thanks to a large amount of out-of-the-box capabilities for running the right tests.

Using Hypothesis Testing for Successful Product Management

In order to continuously deliver value to users, it is imperative to test various hypotheses, for the purpose of which several types of product hypothesis testing can be employed. Each hypothesis needs to have an accompanying test design, as described above, in order to conclusively validate or invalidate it.

This approach helps to quantify the value delivered by new changes and features, bring focus to the most valuable features, and deliver incremental iterations.

• How to Conduct Remote User Interviews [Infographic]
• A/B Testing UX for Component-based Frameworks
• Building an AI Product? Maximize Value With an Implementation Framework

Further Reading on the Toptal Blog:

• Evolving UX: Experimental Product Design with a CXO
• How to Conduct Usability Testing in Six Steps
• 3 Product-led Growth Frameworks to Build Your Business
• A Product Designer’s Guide to Competitive Analysis

Understanding the basics

What is a product hypothesis.

A product hypothesis is an assumption that some improvement in the product will bring an increase in important metrics like revenue or product usage statistics.

What are the three required parts of a hypothesis?

The three required parts of a hypothesis are the assumption, the condition, and the prediction.

Why do we do A/B testing?

We do A/B testing to make sure that any improvement in the product increases our tracked metrics.

What is A/B testing used for?

A/B testing is used to check if our product improvements create the desired change in metrics.

What is A/B testing and multivariate testing?

A/B testing and multivariate testing are types of hypothesis testing. A/B testing checks how important metrics change with and without a single change in the product. Multivariate testing can track multiple variations of the same product improvement.

Kumara Raghavendra

Dubai, United Arab Emirates

Member since August 6, 2019

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What Is Product Management Hypothesis?

• 1.  What Is Product Management?
• 2.  What Is a Software Product?
• 3.  Software Product Manager
• 4.  Product Owner
• 5.  Product Management Life Cycle
• 6.  Product Management Roadmap
• 7.  Product Management Software and Tools
• 8.  Product Backlog
• 9.  Product Management OKRs
• 10.  Product Requirements Documents
• 11.  Product Management Metrics and KPIs Explained
• 12.  Product Analytics
• 13.  Comprehensive Guide to Lean Product Management
• 14.  Best Product Management Resources for Product Managers
• 15.  Practical Product Management Templates
• 16.  FAQ
• 17.  Glossary of Product Management Terms

The path to creating a great product can be riddled with unknowns.

To create a successful product that delivers value to customers, product teams grapple with many questions such as:

• Who is our ideal customer?
• What is the most important product feature to build?
• Will customers like a specific feature?

Using a scientific process for product management can help funnel these assumptions into actionable and specific hypotheses. Then, teams can validate their ideas and make the product more valuable for the end-user.

In this article, we’ll learn more about the product management hypothesis and how it can help create successful products consistently.

Product management hypothesis definition

Product management hypothesis is a scientific process that guides teams to test different product ideas and evaluate their merit. It helps them prioritize their finite energy, time, development resources, and budget.

To create hypotheses , product teams can be inspired by multiple sources, including:

• Observations and events happening around them
• Personal opinions of team members
• Earlier experiences of building and launching a different product
• An evaluation and assessment that leads to the identification of unique patterns in data

The most creative ideas can come when teams collaborate. When ideas are identified and expanded, they become hypotheses.

How does the product management hypothesis work?

A method has as many variations as its users. The product management hypothesis has evolved over the years, but here is a brief outline of how it works.

• Identify an idea, assumption, or observation.
• Question the idea or observation to learn more about it.
• Create an entire hypothesis and explain the idea, observation, or assumption.
• Outline a prediction about the hypothesis.
• Test the prediction.
• Review testing results to iterate and create new hypotheses

Product management hypothesis checklist

When time is limited, teams cannot spend too long creating a hypothesis.

That’s why having a well-planned product management checklist can help in identifying good hypotheses quickly. A good hypothesis is an idea or assumption that:

• Is believed to be true, but whose merit needs to be assessed
• Can be tested in many ways
• Is expected to occur in the near future
• Can be true or false
• Applies to the ideal end-users of the product
• Is measurable and identifiable

Product management hypothesis example

Here’s a simple template to outline your product management hypothesis:

• The core idea, assumption, or observation 
• The potential impact this idea will have
• Who will this idea impact the most?
• What will be the estimated volume and nature of the impact?
• When will the idea and its impact occur? 

Here’s an example of a product management hypothesis:

• Idea: We want to redesign the web user interface for a SaaS product to increase conversions
• Potential impact: This redesign targets to increase conversions for new users 
• The audience of impact: Showcase the redesign only to new users to understand the impact on conversions (there’s no point in showing this to existing users since the goal here is new user conversions)
• Impact volume: The targeted volume of the redesign-led conversions will be 35%
• Time period: The redesign testing would take three weeks, starting from August 15

Stop guessing which feature or product to prioritize and build. Use the product management hypothesis as a guide to finding your next successful product or feature ideas. 

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Further reading

How to Create a Product Roadmap

Product Backlog

Product Owner

Product Life Cycle

• Product Management Strategy
• Defining Software Product Strategy
• Product Management Launch Plan
• Product Management Goals
• Product Roadmap

Product Requirements

• Defining Product Specifications
• Writing Software Requirements
• Product Design Requirement Document

Product Management Team And Roles

• Product Management Hierarchy
• Product Management Team and Roles
• Role of a Product Management Lead
• Role of a Product Management Specialist
• Product Manager vs Software Engineer
• Technical Product Manager vs Product Manager
• How to Become a Product Owner
• Project Manager vs Project Owner
• Importance of The Product Owner

Product Management Software & Tools

• Product Management Dashboard
• Product Management Maturity Model
• Product Management Software
• Product Management Workflow

12 min read

Value Hypothesis 101: A Product Manager's Guide

Talk to Sales

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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A Beginner’s Guide to Hypothesis Testing in Business

• 30 Mar 2021

Becoming a more data-driven decision-maker can bring several benefits to your organization, enabling you to identify new opportunities to pursue and threats to abate. Rather than allowing subjective thinking to guide your business strategy, backing your decisions with data can empower your company to become more innovative and, ultimately, profitable.

If you’re new to data-driven decision-making, you might be wondering how data translates into business strategy. The answer lies in generating a hypothesis and verifying or rejecting it based on what various forms of data tell you.

Below is a look at hypothesis testing and the role it plays in helping businesses become more data-driven.

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What Is Hypothesis Testing?

To understand what hypothesis testing is, it’s important first to understand what a hypothesis is.

A hypothesis or hypothesis statement seeks to explain why something has happened, or what might happen, under certain conditions. It can also be used to understand how different variables relate to each other. Hypotheses are often written as if-then statements; for example, “If this happens, then this will happen.”

Hypothesis testing , then, is a statistical means of testing an assumption stated in a hypothesis. While the specific methodology leveraged depends on the nature of the hypothesis and data available, hypothesis testing typically uses sample data to extrapolate insights about a larger population.

Hypothesis Testing in Business

When it comes to data-driven decision-making, there’s a certain amount of risk that can mislead a professional. This could be due to flawed thinking or observations, incomplete or inaccurate data , or the presence of unknown variables. The danger in this is that, if major strategic decisions are made based on flawed insights, it can lead to wasted resources, missed opportunities, and catastrophic outcomes.

The real value of hypothesis testing in business is that it allows professionals to test their theories and assumptions before putting them into action. This essentially allows an organization to verify its analysis is correct before committing resources to implement a broader strategy.

As one example, consider a company that wishes to launch a new marketing campaign to revitalize sales during a slow period. Doing so could be an incredibly expensive endeavor, depending on the campaign’s size and complexity. The company, therefore, may wish to test the campaign on a smaller scale to understand how it will perform.

In this example, the hypothesis that’s being tested would fall along the lines of: “If the company launches a new marketing campaign, then it will translate into an increase in sales.” It may even be possible to quantify how much of a lift in sales the company expects to see from the effort. Pending the results of the pilot campaign, the business would then know whether it makes sense to roll it out more broadly.

Related: 9 Fundamental Data Science Skills for Business Professionals

Key Considerations for Hypothesis Testing

1. alternative hypothesis and null hypothesis.

In hypothesis testing, the hypothesis that’s being tested is known as the alternative hypothesis . Often, it’s expressed as a correlation or statistical relationship between variables. The null hypothesis , on the other hand, is a statement that’s meant to show there’s no statistical relationship between the variables being tested. It’s typically the exact opposite of whatever is stated in the alternative hypothesis.

For example, consider a company’s leadership team that historically and reliably sees $12 million in monthly revenue. They want to understand if reducing the price of their services will attract more customers and, in turn, increase revenue.

In this case, the alternative hypothesis may take the form of a statement such as: “If we reduce the price of our flagship service by five percent, then we’ll see an increase in sales and realize revenues greater than $12 million in the next month.”

The null hypothesis, on the other hand, would indicate that revenues wouldn’t increase from the base of $12 million, or might even decrease.

Check out the video below about the difference between an alternative and a null hypothesis, and subscribe to our YouTube channel for more explainer content.

2. Significance Level and P-Value

Statistically speaking, if you were to run the same scenario 100 times, you’d likely receive somewhat different results each time. If you were to plot these results in a distribution plot, you’d see the most likely outcome is at the tallest point in the graph, with less likely outcomes falling to the right and left of that point.

With this in mind, imagine you’ve completed your hypothesis test and have your results, which indicate there may be a correlation between the variables you were testing. To understand your results' significance, you’ll need to identify a p-value for the test, which helps note how confident you are in the test results.

In statistics, the p-value depicts the probability that, assuming the null hypothesis is correct, you might still observe results that are at least as extreme as the results of your hypothesis test. The smaller the p-value, the more likely the alternative hypothesis is correct, and the greater the significance of your results.

3. One-Sided vs. Two-Sided Testing

When it’s time to test your hypothesis, it’s important to leverage the correct testing method. The two most common hypothesis testing methods are one-sided and two-sided tests , or one-tailed and two-tailed tests, respectively.

Typically, you’d leverage a one-sided test when you have a strong conviction about the direction of change you expect to see due to your hypothesis test. You’d leverage a two-sided test when you’re less confident in the direction of change.

4. Sampling

To perform hypothesis testing in the first place, you need to collect a sample of data to be analyzed. Depending on the question you’re seeking to answer or investigate, you might collect samples through surveys, observational studies, or experiments.

A survey involves asking a series of questions to a random population sample and recording self-reported responses.

Observational studies involve a researcher observing a sample population and collecting data as it occurs naturally, without intervention.

Finally, an experiment involves dividing a sample into multiple groups, one of which acts as the control group. For each non-control group, the variable being studied is manipulated to determine how the data collected differs from that of the control group.

Learn How to Perform Hypothesis Testing

Hypothesis testing is a complex process involving different moving pieces that can allow an organization to effectively leverage its data and inform strategic decisions.

If you’re interested in better understanding hypothesis testing and the role it can play within your organization, one option is to complete a course that focuses on the process. Doing so can lay the statistical and analytical foundation you need to succeed.

Do you want to learn more about hypothesis testing? Explore Business Analytics —one of our online business essentials courses —and download our Beginner’s Guide to Data & Analytics .

About the Author

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• What is product-market fit?

Product-market fit: Essential steps to measure and achieve it

Product-market fit describes how well your product meets the demand of a viable market. (That is a textbook definition, anyway.) Here is another definition that we like better: Product-market fit is when your product is something that people really want to buy and you have enough customers to support your growth over time.

Finding product-market fit is vital to getting product development right. Maybe this seems obvious — who would build a product that no one wants to buy? No one. At least, not intentionally. But it happens more often than you might think.

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You can find countless cautionary tales. Many well-meaning founders and product builders have put passion above product-market fit. Those that do so often confuse their own excitement for a solution to a problem that may not exist in the market. If a product does not fit, it tends to fail.

Product-market fit is essential to lasting product success. It is like a special ingredient with no substitutions. But the process of finding it (and recognizing when you have) is not always straightforward. Many product experts have shared their take on what product-market fit really looks like. As a product builder, it is wise to seek out varied perspectives to inform your understanding of the topic. But this guide will walk you through the fundamentals as a start.

Skip ahead to any section:

Why is product-market fit important?

Components of product-market fit

How to find product-market fit

Who is responsible for product-market fit, how to measure product-market fit.

Product-market fit examples

You can have a brilliant idea. A compelling story. A talented product team. And even a fully functional product that manifests all of these things. But if no one wants to buy what you are offering, then you will not get anywhere. This is why product-market fit matters.

While you may think product-market fit is primarily a startup issue, it is just as important for established companies with successful product portfolios . New and existing products require the same level of attention to product-market fit.

Some startups fall into the trap of conflating early enthusiasm from a few people with a real market opportunity. For established businesses, the challenge is looking beyond positive feedback and requests from a few to an actual need by many. No matter how innovative you believe your idea is or how badly someone wishes your product had a certain feature — this does not mean you should build it.

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Product-led vs. engineering-led organizations

In both scenarios, product-market fit is a counterbalance to egos and excitement. If you cannot confirm that enough demand exists to sustain growth and eventually turn a profit, it will be hard to win. Maybe even impossible. Finding product-market fit requires you to question what you think you know and prove how you will succeed. This is challenging, ongoing work — it is core to ensuring your long-term success and continuously delivering product value .

Of course, those other elements still contribute. A solid strategy , bold leadership, and a high-achieving product development team — getting all of these strong building blocks in place bodes well for the future of any business.

Certain frameworks can also help you to validate your likelihood of product-market fit success. For example, completing a proof of concept upfront is one way to examine the feasibility of your product idea from multiple angles. Aha! software includes this template:

Start using this template now

What are the components of product-market fit?

Product-market fit has two main components: your product and the market. Self-explanatory? Yes. But these components are usually expressed in terms of value and growth. Both can be written as a hypothesis explaining what you believe will drive success.

Product-market fit can be elusive. Some product builders will describe it as the stuff of legends but it is certainly obtainable. You can assume that any highly successful company has found product-market fit at least once.

There is no rulebook but many successful companies follow similar pathways to find product-market fit. Above all, the most important component is market research . You need extensive, up-to-date knowledge of potential markets to unearth viable opportunities.

This comes with a few caveats. Sometimes a market will not yet exist for your product. It is difficult to establish a new market but it has been done — though it requires much greater innovation, customer research, and confidence to do so. In other cases, external factors like world events or faster competitors may affect your success, even in a good market.

With that in mind, here are five steps to finding product-market fit that apply to most scenarios:

Identify the growth opportunity. First, conduct market research to find a good market. The definition of a "good market" can vary, but usually means there is a sufficient number of existing users, potential for growth, and ease of customer acquisition. Writing a market requirements document can help you articulate the market opportunity. Summarize the results in your growth hypothesis.

Understand customer needs. Dig into your target market and research potential customers . Seek to understand their deepest pain points and underserved needs. Then, develop customer personas based on what you learn.

Define product value. Think hard about what value means to your business — then capture how you plan to deliver it and solve the problems you have identified in the market by forming your value hypothesis.

Build value first. Develop an initial version of your product to start validating your hypotheses. (You may know this as a Minimum Viable Product — or a Minimum Lovable Product as we call it.)

Iterate and adapt as necessary. Despite your best research, you may not achieve product-market fit on the first try. Even if you do — it takes diligence to maintain. Keep working to improve your understanding of market opportunities and enhancing your product to meet them.

This varies by company stage. In startups, finding product-market fit is what founders do. Much of it falls under executive leadership in established companies too as part of setting the overall business strategy. Product management also plays a considerable role — conducting customer and market research, validating ideas and opportunities, and delivering the product itself. Typically all of these inputs will be used to do a formal analysis of the product's strengths, weaknesses, opportunities, and threats (SWOT).

You can conduct a SWOT analysis of your own using the whiteboard template below. (A similar template is also built into Aha! Roadmaps .)

Start using this template now

That said, product-market fit should be the concern of the entire product development team . It affects everyone's ability to deliver value to customers and meet business goals. For example, engineering should consider how the product's technical foundation supports user needs — while product marketing must consider how product positioning will resonate with the target market.

It can be difficult to pinpoint successful product-market fit. Some say to measure it with benchmarks while others describe it as a feeling . This ambiguity is tricky — it can even lead some companies to believe they have found product-market fit when they have not. But the most obvious sign of product-market fit is a steady stream of customers excitedly buying from you.

Keep in mind that product-market fit does not suddenly happen one day. Reaching it is gradual — it relies on you consistently prioritizing, delivering, and measuring product value in your decision-making. Practicing value-based product development is integral to how successful companies encourage this deeper level of thinking.

In addition, some other metrics and methods can be indicators of product-market fit:

Growing demand for new product features

Major increases in product usage

High Net Promoter Scores (NPS)

Responses to customer survey questions (e.g., "Would you be disappointed if you could no longer use this product?")

Long-term customer retention and/or low churn

Examples of product-market fit

We can learn from plenty of good product-market fit examples (Uber, Netflix, and Slack) and bad product-market fit examples (Quibi, Segway, and MoviePass). You can also read about examples of very similar products that failed at first but gained traction later on with better product-market fit (Vine and TikTok).

Many of these examples are of failed startups, but as we mentioned before, product-market fit is just as pertinent for established companies. Let's look at two examples from Google. Google has experienced both sides of the product-market fit problem. The examples below help to show how product-market fit is usually a greater success factor than ambitious technology, well-known branding, vast resources, and even highly-skilled development teams.

Finding product-market fit and building a product that fulfills customer needs is complex work. If your team is still using disparate tools like spreadsheets and documents to store information, it can be challenging to connect the dots between your research, strategy, and daily product work.

That is why most modern product development teams rely on purpose-built software that brings all of these elements together in one place — so you can create real value for customers and the business.

Aha! is the world's #1 product development software. Start a free 30-day trial today .

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7.1: Basics of Hypothesis Testing

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• Page ID 16360

• Kathryn Kozak
• Coconino Community College

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To understand the process of a hypothesis tests, you need to first have an understanding of what a hypothesis is, which is an educated guess about a parameter. Once you have the hypothesis, you collect data and use the data to make a determination to see if there is enough evidence to show that the hypothesis is true. However, in hypothesis testing you actually assume something else is true, and then you look at your data to see how likely it is to get an event that your data demonstrates with that assumption. If the event is very unusual, then you might think that your assumption is actually false. If you are able to say this assumption is false, then your hypothesis must be true. This is known as a proof by contradiction. You assume the opposite of your hypothesis is true and show that it can’t be true. If this happens, then your hypothesis must be true. All hypothesis tests go through the same process. Once you have the process down, then the concept is much easier. It is easier to see the process by looking at an example. Concepts that are needed will be detailed in this example.

Example \(\PageIndex{1}\) basics of hypothesis testing

Suppose a manufacturer of the XJ35 battery claims the mean life of the battery is 500 days with a standard deviation of 25 days. You are the buyer of this battery and you think this claim is inflated. You would like to test your belief because without a good reason you can’t get out of your contract.

What do you do?

Well first, you should know what you are trying to measure. Define the random variable.

Let x = life of a XJ35 battery

Now you are not just trying to find different x values. You are trying to find what the true mean is. Since you are trying to find it, it must be unknown. You don’t think it is 500 days. If you did, you wouldn’t be doing any testing. The true mean, \(\mu\), is unknown. That means you should define that too.

Let \(\mu\)= mean life of a XJ35 battery

You may want to collect a sample. What kind of sample?

You could ask the manufacturers to give you batteries, but there is a chance that there could be some bias in the batteries they pick. To reduce the chance of bias, it is best to take a random sample.

How big should the sample be?

A sample of size 30 or more means that you can use the central limit theorem. Pick a sample of size 30.

Example \(\PageIndex{1}\) contains the data for the sample you collected:

Now what should you do? Looking at the data set, you see some of the times are above 500 and some are below. But looking at all of the numbers is too difficult. It might be helpful to calculate the mean for this sample.

The sample mean is \(\overline{x} = 490\) days. Looking at the sample mean, one might think that you are right. However, the standard deviation and the sample size also plays a role, so maybe you are wrong.

Before going any farther, it is time to formalize a few definitions.

You have a guess that the mean life of a battery is less than 500 days. This is opposed to what the manufacturer claims. There really are two hypotheses, which are just guesses here – the one that the manufacturer claims and the one that you believe. It is helpful to have names for them.

Definition \(\PageIndex{1}\)

Null Hypothesis : historical value, claim, or product specification. The symbol used is \(H_{o}\).

Definition \(\PageIndex{2}\)

Alternate Hypothesis : what you want to prove. This is what you want to accept as true when you reject the null hypothesis. There are two symbols that are commonly used for the alternative hypothesis: \(H_{A}\) or \(H_{I}\). The symbol \(H_{A}\) will be used in this book.

In general, the hypotheses look something like this:

\(H_{o} : \mu=\mu_{o}\)

\(H_{A} : \mu<\mu_{o}\)

where \(\mu_{o}\) just represents the value that the claim says the population mean is actually equal to.

Also, \(H_{A}\) can be less than, greater than, or not equal to.

For this problem:

\(H_{o} : \mu=500\) days, since the manufacturer says the mean life of a battery is 500 days.

\(H_{A} : \mu<500\) days, since you believe that the mean life of the battery is less than 500 days.

Now back to the mean. You have a sample mean of 490 days. Is this small enough to believe that you are right and the manufacturer is wrong? How small does it have to be?

If you calculated a sample mean of 235, you would definitely believe the population mean is less than 500. But even if you had a sample mean of 435 you would probably believe that the true mean was less than 500. What about 475? Or 483? There is some point where you would stop being so sure that the population mean is less than 500. That point separates the values of where you are sure or pretty sure that the mean is less than 500 from the area where you are not so sure. How do you find that point?

Well it depends on how much error you want to make. Of course you don’t want to make any errors, but unfortunately that is unavoidable in statistics. You need to figure out how much error you made with your sample. Take the sample mean, and find the probability of getting another sample mean less than it, assuming for the moment that the manufacturer is right. The idea behind this is that you want to know what is the chance that you could have come up with your sample mean even if the population mean really is 500 days.

You want to find \(P\left(\overline{x}<490 | H_{o} \text { is true }\right)=P(\overline{x}<490 | \mu=500)\)

To compute this probability, you need to know how the sample mean is distributed. Since the sample size is at least 30, then you know the sample mean is approximately normally distributed. Remember \(\mu_{\overline{x}}=\mu\) and \(\sigma_{\overline{x}}=\dfrac{\sigma}{\sqrt{n}}\)

A picture is always useful.

Before calculating the probability, it is useful to see how many standard deviations away from the mean the sample mean is. Using the formula for the z-score from chapter 6, you find

\(z=\dfrac{\overline{x}-\mu_{o}}{\sigma / \sqrt{n}}=\dfrac{490-500}{25 / \sqrt{30}}=-2.19\)

This sample mean is more than two standard deviations away from the mean. That seems pretty far, but you should look at the probability too.

On TI-83/84:

\(P(\overline{x}<490 | \mu=500)=\text { normalcdf }(-1 E 99,490,500,25 \div \sqrt{30}) \approx 0.0142\)

\(P(\overline{x}<490 \mu=500)=\text { pnorm }(490,500,25 / \operatorname{sqrt}(30)) \approx 0.0142\)

There is a 1.42% chance that you could find a sample mean less than 490 when the population mean is 500 days. This is really small, so the chances are that the assumption that the population mean is 500 days is wrong, and you can reject the manufacturer’s claim. But how do you quantify really small? Is 5% or 10% or 15% really small? How do you decide?

Before you answer that question, a couple more definitions are needed.

Definition \(\PageIndex{3}\)

Test Statistic : \(z=\dfrac{\overline{x}-\mu_{o}}{\sigma / \sqrt{n}}\) since it is calculated as part of the testing of the hypothesis.

Definition \(\PageIndex{4}\)

p – value : probability that the test statistic will take on more extreme values than the observed test statistic, given that the null hypothesis is true. It is the probability that was calculated above.

Now, how small is small enough? To answer that, you really want to know the types of errors you can make.

There are actually only two errors that can be made. The first error is if you say that \(H_{o}\) is false, when in fact it is true. This means you reject \(H_{o}\) when \(H_{o}\) was true. The second error is if you say that \(H_{o}\) is true, when in fact it is false. This means you fail to reject \(H_{o}\) when \(H_{o}\) is false. The following table organizes this for you:

Type of errors:

Definition \(\PageIndex{5}\)

Type I Error is rejecting \(H_{o}\) when \(H_{o}\) is true, and

Definition \(\PageIndex{6}\)

Type II Error is failing to reject \(H_{o}\) when \(H_{o}\) is false.

Since these are the errors, then one can define the probabilities attached to each error.

Definition \(\PageIndex{7}\)

\(\alpha\) = P(type I error) = P(rejecting \(H_{o} / H_{o}\) is true)

Definition \(\PageIndex{8}\)

\(\beta\) = P(type II error) = P(failing to reject \(H_{o} / H_{o}\) is false)

\(\alpha\) is also called the level of significance .

Another common concept that is used is Power = \(1-\beta \).

Now there is a relationship between \(\alpha\) and \(\beta\). They are not complements of each other. How are they related?

If \(\alpha\) increases that means the chances of making a type I error will increase. It is more likely that a type I error will occur. It makes sense that you are less likely to make type II errors, only because you will be rejecting \(H_{o}\) more often. You will be failing to reject \(H_{o}\) less, and therefore, the chance of making a type II error will decrease. Thus, as \(\alpha\) increases, \(\beta\) will decrease, and vice versa. That makes them seem like complements, but they aren’t complements. What gives? Consider one more factor – sample size.

Consider if you have a larger sample that is representative of the population, then it makes sense that you have more accuracy then with a smaller sample. Think of it this way, which would you trust more, a sample mean of 490 if you had a sample size of 35 or sample size of 350 (assuming a representative sample)? Of course the 350 because there are more data points and so more accuracy. If you are more accurate, then there is less chance that you will make any error. By increasing the sample size of a representative sample, you decrease both \(\alpha\) and \(\beta\).

Summary of all of this:

• For a certain sample size, n , if \(\alpha\) increases, \(\beta\) decreases.
• For a certain level of significance, \(\alpha\), if n increases, \(\beta\) decreases.

Now how do you find \(\alpha\) and \(\beta\)? Well \(\alpha\) is actually chosen. There are only three values that are usually picked for \(\alpha\): 0.01, 0.05, and 0.10. \(\beta\) is very difficult to find, so usually it isn’t found. If you want to make sure it is small you take as large of a sample as you can afford provided it is a representative sample. This is one use of the Power. You want \(\beta\) to be small and the Power of the test is large. The Power word sounds good.

Which pick of \(\alpha\) do you pick? Well that depends on what you are working on. Remember in this example you are the buyer who is trying to get out of a contract to buy these batteries. If you create a type I error, you said that the batteries are bad when they aren’t, most likely the manufacturer will sue you. You want to avoid this. You might pick \(\alpha\) to be 0.01. This way you have a small chance of making a type I error. Of course this means you have more of a chance of making a type II error. No big deal right? What if the batteries are used in pacemakers and you tell the person that their pacemaker’s batteries are good for 500 days when they actually last less, that might be bad. If you make a type II error, you say that the batteries do last 500 days when they last less, then you have the possibility of killing someone. You certainly do not want to do this. In this case you might want to pick \(\alpha\) as 0.10. If both errors are equally bad, then pick \(\alpha\) as 0.05.

The above discussion is why the choice of \(\alpha\) depends on what you are researching. As the researcher, you are the one that needs to decide what \(\alpha\) level to use based on your analysis of the consequences of making each error is.

If a type I error is really bad, then pick \(\alpha\) = 0.01.

If a type II error is really bad, then pick \(\alpha\) = 0.10

If neither error is bad, or both are equally bad, then pick \(\alpha\) = 0.05

The main thing is to always pick the \(\alpha\) before you collect the data and start the test.

The above discussion was long, but it is really important information. If you don’t know what the errors of the test are about, then there really is no point in making conclusions with the tests. Make sure you understand what the two errors are and what the probabilities are for them.

Now it is time to go back to the example and put this all together. This is the basic structure of testing a hypothesis, usually called a hypothesis test. Since this one has a test statistic involving z, it is also called a z-test. And since there is only one sample, it is usually called a one-sample z-test.

Example \(\PageIndex{2}\) battery example revisited

• State the random variable and the parameter in words.
• State the null and alternative hypothesis and the level of significance.
• A random sample of size n is taken.
• The population standard derivation is known.
• The sample size is at least 30 or the population of the random variable is normally distributed.
• Find the sample statistic, test statistic, and p-value.
• Interpretation

1. x = life of battery

\(\mu\) = mean life of a XJ35 battery

2. \(H_{o} : \mu=500\) days

\(H_{A} : \mu<500\) days

\(\alpha = 0.10\) (from above discussion about consequences)

3. Every hypothesis has some assumptions that be met to make sure that the results of the hypothesis are valid. The assumptions are different for each test. This test has the following assumptions.

• This occurred in this example, since it was stated that a random sample of 30 battery lives were taken.
• This is true, since it was given in the problem.
• The sample size was 30, so this condition is met.

4. The test statistic depends on how many samples there are, what parameter you are testing, and assumptions that need to be checked. In this case, there is one sample and you are testing the mean. The assumptions were checked above.

Sample statistic:

\(\overline{x} = 490\)

Test statistic:

Using TI-83/84:

\(P(\overline{x}<490 | \mu=500)=\text { normalcdf }(-1 \mathrm{E} 99,490,500,25 / \sqrt{30}) \approx 0.0142\)

\(P(\overline{x}<490 | \mu=500)=\operatorname{pnorm}(490,500,25 / \operatorname{sqrt}(30)) \approx 0.0142\)

5. Now what? Well, this p-value is 0.0142. This is a lot smaller than the amount of error you would accept in the problem -\(\alpha\) = 0.10. That means that finding a sample mean less than 490 days is unusual to happen if \(H_{o}\) is true. This should make you think that \(H_{o}\) is not true. You should reject \(H_{o}\).

In fact, in general:

Reject \(H_{o}\) if the p-value < \(\alpha\) and

Fail to reject \(H_{o}\) if the p-value \(\geq \alpha\).

6. Since you rejected \(H_{o}\), what does this mean in the real world? That is what goes in the interpretation. Since you rejected the claim by the manufacturer that the mean life of the batteries is 500 days, then you now can believe that your hypothesis was correct. In other words, there is enough evidence to show that the mean life of the battery is less than 500 days.

Now that you know that the batteries last less than 500 days, should you cancel the contract? Statistically, there is evidence that the batteries do not last as long as the manufacturer says they should. However, based on this sample there are only ten days less on average that the batteries last. There may not be practical significance in this case. Ten days do not seem like a large difference. In reality, if the batteries are used in pacemakers, then you would probably tell the patient to have the batteries replaced every year. You have a large buffer whether the batteries last 490 days or 500 days. It seems that it might not be worth it to break the contract over ten days. What if the 10 days was practically significant? Are there any other things you should consider? You might look at the business relationship with the manufacturer. You might also look at how much it would cost to find a new manufacturer. These are also questions to consider before making any changes. What this discussion should show you is that just because a hypothesis has statistical significance does not mean it has practical significance. The hypothesis test is just one part of a research process. There are other pieces that you need to consider.

That’s it. That is what a hypothesis test looks like. All hypothesis tests are done with the same six steps. Those general six steps are outlined below.

• State the random variable and the parameter in words. This is where you are defining what the unknowns are in this problem. x = random variable \(\mu\) = mean of random variable, if the parameter of interest is the mean. There are other parameters you can test, and you would use the appropriate symbol for that parameter.
• State the null and alternative hypotheses and the level of significance \(H_{o} : \mu=\mu_{o}\), where \(\mu_{o}\) is the known mean \(H_{A} : \mu<\mu_{o}\) \(H_{A} : \mu>\mu_{o}\), use the appropriate one for your problem \(H_{A} : \mu \neq \mu_{o}\) Also, state your \(\alpha\) level here.
• State and check the assumptions for a hypothesis test. Each hypothesis test has its own assumptions. They will be stated when the different hypothesis tests are discussed.
• Find the sample statistic, test statistic, and p-value. This depends on what parameter you are working with, how many samples, and the assumptions of the test. The p-value depends on your \(H_{A}\). If you are doing the \(H_{A}\) with the less than, then it is a left-tailed test, and you find the probability of being in that left tail. If you are doing the \(H_{A}\) with the greater than, then it is a right-tailed test, and you find the probability of being in the right tail. If you are doing the \(H_{A}\) with the not equal to, then you are doing a two-tail test, and you find the probability of being in both tails. Because of symmetry, you could find the probability in one tail and double this value to find the probability in both tails.
• Conclusion This is where you write reject \(H_{o}\) or fail to reject \(H_{o}\). The rule is: if the p-value < \(\alpha\), then reject \(H_{o}\). If the p-value \(\geq \alpha\), then fail to reject \(H_{o}\).
• Interpretation This is where you interpret in real world terms the conclusion to the test. The conclusion for a hypothesis test is that you either have enough evidence to show \(H_{A}\) is true, or you do not have enough evidence to show \(H_{A}\) is true.

Sorry, one more concept about the conclusion and interpretation. First, the conclusion is that you reject \(H_{o}\) or you fail to reject \(H_{o}\). Why was it said like this? It is because you never accept the null hypothesis. If you wanted to accept the null hypothesis, then why do the test in the first place? In the interpretation, you either have enough evidence to show \(H_{A}\) is true, or you do not have enough evidence to show \(H_{A}\) is true. You wouldn’t want to go to all this work and then find out you wanted to accept the claim. Why go through the trouble? You always want to show that the alternative hypothesis is true. Sometimes you can do that and sometimes you can’t. It doesn’t mean you proved the null hypothesis; it just means you can’t prove the alternative hypothesis. Here is an example to demonstrate this.

Example \(\PageIndex{3}\) conclusion in hypothesis tests

In the U.S. court system a jury trial could be set up as a hypothesis test. To really help you see how this works, let’s use OJ Simpson as an example. In the court system, a person is presumed innocent until he/she is proven guilty, and this is your null hypothesis. OJ Simpson was a football player in the 1970s. In 1994 his ex-wife and her friend were killed. OJ Simpson was accused of the crime, and in 1995 the case was tried. The prosecutors wanted to prove OJ was guilty of killing his wife and her friend, and that is the alternative hypothesis

\(H_{0}\): OJ is innocent of killing his wife and her friend

\(H_{A}\): OJ is guilty of killing his wife and her friend

In this case, a verdict of not guilty was given. That does not mean that he is innocent of this crime. It means there was not enough evidence to prove he was guilty. Many people believe that OJ was guilty of this crime, but the jury did not feel that the evidence presented was enough to show there was guilt. The verdict in a jury trial is always guilty or not guilty!

The same is true in a hypothesis test. There is either enough or not enough evidence to show that alternative hypothesis. It is not that you proved the null hypothesis true.

When identifying hypothesis, it is important to state your random variable and the appropriate parameter you want to make a decision about. If count something, then the random variable is the number of whatever you counted. The parameter is the proportion of what you counted. If the random variable is something you measured, then the parameter is the mean of what you measured. (Note: there are other parameters you can calculate, and some analysis of those will be presented in later chapters.)

Example \(\PageIndex{4}\) stating hypotheses

Identify the hypotheses necessary to test the following statements:

• The average salary of a teacher is more than $30,000.
• The proportion of students who like math is less than 10%.
• The average age of students in this class differs from 21.

a. x = salary of teacher

\(\mu\) = mean salary of teacher

The guess is that \(\mu>\$ 30,000\) and that is the alternative hypothesis.

The null hypothesis has the same parameter and number with an equal sign.

\(\begin{array}{l}{H_{0} : \mu=\$ 30,000} \\ {H_{A} : \mu>\$ 30,000}\end{array}\)

b. x = number od students who like math

p = proportion of students who like math

The guess is that p < 0.10 and that is the alternative hypothesis.

\(\begin{array}{l}{H_{0} : p=0.10} \\ {H_{A} : p<0.10}\end{array}\)

c. x = age of students in this class

\(\mu\) = mean age of students in this class

The guess is that \(\mu \neq 21\) and that is the alternative hypothesis.

\(\begin{array}{c}{H_{0} : \mu=21} \\ {H_{A} : \mu \neq 21}\end{array}\)

Example \(\PageIndex{5}\) Stating Type I and II Errors and Picking Level of Significance

• The plant-breeding department at a major university developed a new hybrid raspberry plant called YumYum Berry. Based on research data, the claim is made that from the time shoots are planted 90 days on average are required to obtain the first berry with a standard deviation of 9.2 days. A corporation that is interested in marketing the product tests 60 shoots by planting them and recording the number of days before each plant produces its first berry. The sample mean is 92.3 days. The corporation wants to know if the mean number of days is more than the 90 days claimed. State the type I and type II errors in terms of this problem, consequences of each error, and state which level of significance to use.
• A concern was raised in Australia that the percentage of deaths of Aboriginal prisoners was higher than the percent of deaths of non-indigenous prisoners, which is 0.27%. State the type I and type II errors in terms of this problem, consequences of each error, and state which level of significance to use.

a. x = time to first berry for YumYum Berry plant

\(\mu\) = mean time to first berry for YumYum Berry plant

\(\begin{array}{l}{H_{0} : \mu=90} \\ {H_{A} : \mu>90}\end{array}\)

Type I Error: If the corporation does a type I error, then they will say that the plants take longer to produce than 90 days when they don’t. They probably will not want to market the plants if they think they will take longer. They will not market them even though in reality the plants do produce in 90 days. They may have loss of future earnings, but that is all.

Type II error: The corporation do not say that the plants take longer then 90 days to produce when they do take longer. Most likely they will market the plants. The plants will take longer, and so customers might get upset and then the company would get a bad reputation. This would be really bad for the company.

Level of significance: It appears that the corporation would not want to make a type II error. Pick a 10% level of significance, \(\alpha = 0.10\).

b. x = number of Aboriginal prisoners who have died

p = proportion of Aboriginal prisoners who have died

\(\begin{array}{l}{H_{o} : p=0.27 \%} \\ {H_{A} : p>0.27 \%}\end{array}\)

Type I error: Rejecting that the proportion of Aboriginal prisoners who died was 0.27%, when in fact it was 0.27%. This would mean you would say there is a problem when there isn’t one. You could anger the Aboriginal community, and spend time and energy researching something that isn’t a problem.

Type II error: Failing to reject that the proportion of Aboriginal prisoners who died was 0.27%, when in fact it is higher than 0.27%. This would mean that you wouldn’t think there was a problem with Aboriginal prisoners dying when there really is a problem. You risk causing deaths when there could be a way to avoid them.

Level of significance: It appears that both errors may be issues in this case. You wouldn’t want to anger the Aboriginal community when there isn’t an issue, and you wouldn’t want people to die when there may be a way to stop it. It may be best to pick a 5% level of significance, \(\alpha = 0.05\).

Hypothesis testing is really easy if you follow the same recipe every time. The only differences in the various problems are the assumptions of the test and the test statistic you calculate so you can find the p-value. Do the same steps, in the same order, with the same words, every time and these problems become very easy.

Exercise \(\PageIndex{1}\)

For the problems in this section, a question is being asked. This is to help you understand what the hypotheses are. You are not to run any hypothesis tests and come up with any conclusions in this section.

• Eyeglassomatic manufactures eyeglasses for different retailers. They test to see how many defective lenses they made in a given time period and found that 11% of all lenses had defects of some type. Looking at the type of defects, they found in a three-month time period that out of 34,641 defective lenses, 5865 were due to scratches. Are there more defects from scratches than from all other causes? State the random variable, population parameter, and hypotheses.
• According to the February 2008 Federal Trade Commission report on consumer fraud and identity theft, 23% of all complaints in 2007 were for identity theft. In that year, Alaska had 321 complaints of identity theft out of 1,432 consumer complaints ("Consumer fraud and," 2008). Does this data provide enough evidence to show that Alaska had a lower proportion of identity theft than 23%? State the random variable, population parameter, and hypotheses.
• The Kyoto Protocol was signed in 1997, and required countries to start reducing their carbon emissions. The protocol became enforceable in February 2005. In 2004, the mean CO2 emission was 4.87 metric tons per capita. Is there enough evidence to show that the mean CO2 emission is lower in 2010 than in 2004? State the random variable, population parameter, and hypotheses.
• The FDA regulates that fish that is consumed is allowed to contain 1.0 mg/kg of mercury. In Florida, bass fish were collected in 53 different lakes to measure the amount of mercury in the fish. The data for the average amount of mercury in each lake is in Example \(\PageIndex{5}\) ("Multi-disciplinary niser activity," 2013). Do the data provide enough evidence to show that the fish in Florida lakes has more mercury than the allowable amount? State the random variable, population parameter, and hypotheses.
• Eyeglassomatic manufactures eyeglasses for different retailers. They test to see how many defective lenses they made in a given time period and found that 11% of all lenses had defects of some type. Looking at the type of defects, they found in a three-month time period that out of 34,641 defective lenses, 5865 were due to scratches. Are there more defects from scratches than from all other causes? State the type I and type II errors in this case, consequences of each error type for this situation from the perspective of the manufacturer, and the appropriate alpha level to use. State why you picked this alpha level.
• According to the February 2008 Federal Trade Commission report on consumer fraud and identity theft, 23% of all complaints in 2007 were for identity theft. In that year, Alaska had 321 complaints of identity theft out of 1,432 consumer complaints ("Consumer fraud and," 2008). Does this data provide enough evidence to show that Alaska had a lower proportion of identity theft than 23%? State the type I and type II errors in this case, consequences of each error type for this situation from the perspective of the state of Arizona, and the appropriate alpha level to use. State why you picked this alpha level.
• The Kyoto Protocol was signed in 1997, and required countries to start reducing their carbon emissions. The protocol became enforceable in February 2005. In 2004, the mean CO2 emission was 4.87 metric tons per capita. Is there enough evidence to show that the mean CO2 emission is lower in 2010 than in 2004? State the type I and type II errors in this case, consequences of each error type for this situation from the perspective of the agency overseeing the protocol, and the appropriate alpha level to use. State why you picked this alpha level.
• The FDA regulates that fish that is consumed is allowed to contain 1.0 mg/kg of mercury. In Florida, bass fish were collected in 53 different lakes to measure the amount of mercury in the fish. The data for the average amount of mercury in each lake is in Example \(\PageIndex{5}\) ("Multi-disciplinary niser activity," 2013). Do the data provide enough evidence to show that the fish in Florida lakes has more mercury than the allowable amount? State the type I and type II errors in this case, consequences of each error type for this situation from the perspective of the FDA, and the appropriate alpha level to use. State why you picked this alpha level.

1. \(H_{o} : p=0.11, H_{A} : p>0.11\)

3. \(H_{o} : \mu=4.87 \text { metric tons per capita, } H_{A} : \mu<4.87 \text { metric tons per capita }\)

5. See solutions

7. See solutions

What if we found ourselves building something that nobody wanted? In that case, what did it matter if we did it on time and on budget? —Eric Ries, The Lean Startup [1]

An Epic is a significant solution development initiative.

Portfolio epics are typically cross-cutting, typically spanning multiple Value Streams and PIs . To accelerate learning and development and reduce risk, SAFe recommends applying the Lean Startup build-measure-learn cycle for these epics.

This article describes the portfolio epic’s definition, approval, and implementation. Agile Release Train (ART) and Solution Train epics, which follow a similar pattern, are described briefly at the end of this article.

There are two types of epics, each of which may occur at different levels of the Framework. Business epics directly deliver business value, while enabler epics advance the  Architectural Runway  to support upcoming business or technical needs.

It’s important to note that epics are not merely a synonym for projects; they operate quite differently, as Figure 1 highlights.

SAFe discourages using the project funding model (refer to the Lean Portfolio Management article). Instead, the funding to implement epics is allocated directly to the value streams within a portfolio. Moreover, Agile Release Trains (ARTs) develop and deliver epics following the Lean Startup Cycle discussed later in this article (Figure 6).

Defining Epics

Since epics are some of the most significant enterprise investments, stakeholders must agree on their intent and definition. Figure 2 provides an epic hypothesis statement template for capturing, organizing, and communicating critical information about an epic.

Epics above the approval Guardrail are made visible, developed, and managed through the  Portfolio Kanban system, where they proceed through various states of maturity until they’re approved or rejected. Before being committed to implementation, epics require analysis. Epic Owners take responsibility for the critical collaborations needed for Business Epics, while  Enterprise Architects  typically guide the Enabler epics that support the technical considerations for business epics.

Creating the Lean Business Case

The result of the epic analysis is a Lean business case (Figure 3).

The LPM reviews the Lean business case to make a go/no-go decision for the epic. Once approved, portfolio epics move to the Ready state of the Portfolio Kanban. When capacity and budget become available from one or more ARTs, the Epic is pulled into implementation. The Epic Owner is responsible for working with Product  and  Solution Management  and  System Architects  to split the epic into Features or Capabilities during backlog refinement. Epic Owners help prioritize these items in their respective backlogs and have ongoing responsibilities for their development and follow-up.

Defining the MVP

Analysis of an epic includes the definition of a Minimum Viable Product (MVP) for the epic. In the context of SAFe, an MVP is an early and minimal version of a new product or business  Solution  used to prove or disprove the epic hypothesis. Unlike storyboards, prototypes, mockups, wireframes, and other exploratory techniques, the MVP is an actual product that real customers can use to generate validated learning.

Estimating Epic Costs

As Epics progress through the Portfolio Kanban, the LPM team will eventually need to understand the potential investment required to realize the hypothesized value. This analysis requires a meaningful estimate of the cost of the MVP, and the forecasted cost of the full implementation should the epic hypothesis be proven true.

• The  MVP cost  ensures the portfolio is budgeting enough money to prove or disprove the Epic hypothesis. It helps ensure that LPM makes sufficient investments in innovation aligned with Lean budget guardrails.
• The forecasted implementation cost considers ROI analysis, helping determine if the business case is sound, and allows the LPM team to prepare for potential adjustments to value stream budgets.

The Epic owner determines the amount of the MVP’s investment in collaboration with other key stakeholders. This investment should be sufficient to prove or disprove the MVP’s hypothesis. Once approved, the value stream cannot spend more than the defined investment cost to build and evaluate the MVP. If the value stream has evidence that this cost will be exceeded during epic implementation, further work on the epic should be discussed with LPM before exceeding the MVP’s estimated cost.

Estimating Implementation Cost

Considerable strategic efforts often require collaboration with external Suppliers to develop Solutions. The MVP and the anticipated full implementation cost estimates should include internal costs and forecasted external Supplier expenses.

Estimating epics in the early stages can be challenging since there is limited data and learning at this point. As illustrated in Figure 4, ‘T-shirt sizing’ is a simple way to estimate epics, especially in the early stages:

• A cost range is established for each t-shirt size using historical data
• The gaps in the cost ranges reflect the uncertainty of estimates and avoid excessive discussion around edge cases
• Each portfolio must determine the relevant cost range for the t-shirt sizes

The Epic Owner can incrementally refine the total implementation cost as the MVP is built and learning occurs.

Supplier Costs

An epic investment often includes the contribution and cost from suppliers, whether internal or external. Ideally, enterprises engage external suppliers via Agile contracts, which supports estimating the costs of a supplier’s contribution to a specific epic. For more on this topic, see the Agile Contracts  extended guidance article.

Forecasting an Epic’s Duration

While it can be challenging to forecast the duration of an epic implemented by a mix of internal ARTs and external suppliers, an understanding of the forecasted duration of the epic is critical to the proper functioning of the portfolio.

Like an epic’s cost, its duration isn’t easy to forecast as it includes several components, such as internal duration, supplier duration, and the collaborations and interactions between the internal and external teams. Practically, unless the epic is wholly outsourced, LPM can focus on forecasts of the internal ARTs affected by the epic, as they are expected to coordinate work with external suppliers.

Forecasting an epic’s duration requires an understanding of three data points:

• An epic’s estimated size in story points for each affected ART can also be calculated using T-shirt sizes and replacing the cost range with a story point range.
• The historical velocity of the impacted ARTs.
• The percent (%) capacity allocation that ARTs can dedicate to working on the epic. This allocation typically results from negotiation between Product and Solution Management, Epic Owners, and LPM.

In the example shown in Figure 5, a portfolio has a substantial enabler epic that affects three ARTs, and LPM seeks to gain an estimate of the forecasted number of PIs.

ART 1 has estimated the epic’s size as 2,000 – 2,500 points. Product Management determines that ART 1 can allocate 40% of total capacity toward implementing its part of the epic. With a historical velocity of 1,000 story points per PI, ART 1 forecasts between five to seven PIs for the epic.

After repeating these calculations for each ART, the Epic Owner can see that some ARTs will likely be ready to release on demand earlier than others. However, the forecasted duration to deliver the entire epic across all ARTs will likely be between six and eight PIs. If this forecast does not align with business needs, negotiations such as adjusting capacity allocations or increasing the budget for suppliers will ensue. The Epic Owner updates the forecasted completion once work begins on the epic.

Implementing Epics

The SAFe Lean startup strategy recommends a highly iterative build-measure-learn cycle for product innovation and strategic investments. This approach for implementing epics provides the economic and strategic advantages of a Lean startup by managing investment and risk incrementally while leveraging the flow and visibility benefits of SAFe (Figure 6).

Gathering the data necessary to prove or disprove the epic hypothesis is highly iterative. These iterations continue until a data-driven result is obtained or the teams consume the entirety of the MVP budget. In general, the result of a proven hypothesis is an MVP suitable for continued investment by the value streams. Otherwise, any further investment requires the creation of a new epic.

After it’s approved for implementation, the Epic Owner works with the Agile Teams  to begin the development activities needed to realize the business outcomes hypothesis for the epic:

• If the hypothesis is true , the epic enters the persevere state, which will drive more work by implementing additional features and capabilities. ARTs manage any further investment in the epic via ongoing WSJF feature prioritization of the ART Backlog . Local features identified by the ART, and those from the epic, compete during routine WSJF reprioritization.
• If the hypothesis is false , Epic Owners can decide to pivot by creating a new epic for LPM review or dropping the initiative altogether and switching to other work in the backlog.

After evaluating an epic’s hypothesis, it may or may not be considered a portfolio concern. However, the Epic Owner may have ongoing stewardship and follow-up responsibilities.

Lean budgets’ empowerment and decentralized decision-making depend on Guardrails for specific checks and balances. Value stream KPIs and other metrics also support guardrails to keep the LPM informed of the epic’s progress toward meeting its business outcomes hypothesis.

ART and Solution Train Epics

Epics may originate from local ARTs or Solution Trains, often starting as initiatives that warrant LPM attention because of their significant business impact or initiatives that exceed the epic threshold. ART and Solution Train epics may also originate from portfolio epics that must be split to facilitate incremental implementation. Like any other epics, ART and Solution Train epics deserve a Lean business case that captures these significant investments’ purpose and expected benefits. The ART and Solution Train Backlogs article describes methods for managing the flow of local epics that do not meet the criteria for portfolio attention.

Last update: 6 September 2023

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

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

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

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

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

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

Null Hypothesis

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

Alternative Hypothesis

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

Directional Hypothesis

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

Non-directional Hypothesis

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

Statistical Hypothesis

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

Composite Hypothesis

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

Empirical Hypothesis

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

Simple Hypothesis

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

Complex Hypothesis

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

Applications of Hypothesis

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

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

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

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

Conduct a Literature Review

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

Determine the Variables

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

Formulate the Hypothesis

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

Write the Null Hypothesis

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

Refine the Hypothesis

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

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

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

Purpose of Hypothesis

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

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

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

When to use Hypothesis

Here are some common situations in which hypotheses are used:

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

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

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

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

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

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Definition of hypothesis

Did you know.

The Difference Between Hypothesis and Theory

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

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

A hypothesis is usually tentative; it's an assumption or suggestion made strictly for the objective of being tested.

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

In non-scientific use, however, hypothesis and theory are often used interchangeably to mean simply an idea, speculation, or hunch, with theory being the more common choice.

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

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

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

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

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

• proposition
• supposition

hypothesis , theory , law mean a formula derived by inference from scientific data that explains a principle operating in nature.

hypothesis implies insufficient evidence to provide more than a tentative explanation.

theory implies a greater range of evidence and greater likelihood of truth.

law implies a statement of order and relation in nature that has been found to be invariable under the same conditions.

Examples of hypothesis in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'hypothesis.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

Greek, from hypotithenai to put under, suppose, from hypo- + tithenai to put — more at do

1641, in the meaning defined at sense 1a

Phrases Containing hypothesis

• counter - hypothesis
• nebular hypothesis
• null hypothesis
• planetesimal hypothesis
• Whorfian hypothesis

Articles Related to hypothesis

This is the Difference Between a...

This is the Difference Between a Hypothesis and a Theory

In scientific reasoning, they're two completely different things

Dictionary Entries Near hypothesis

hypothermia

hypothesize

Cite this Entry

“Hypothesis.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/hypothesis. Accessed 26 May. 2024.

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

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

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

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

Research Hypothesis 101

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

What is a hypothesis?

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

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

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

Hypothesis: sleep impacts academic performance.

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

But that’s not good enough…

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

What is a research hypothesis?

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

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

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

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

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

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

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

Hypothesis Essential #2: Testability (Provability)

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

For example, consider the hypothesis we mentioned earlier:

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

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

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

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

Defining A Research Hypothesis

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

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

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

What about the null hypothesis?

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

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

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

And there you have it – hypotheses in a nutshell. 

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

Psst... there’s more!

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

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16 Comments

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

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

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

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

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

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

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

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

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

could you please elaborate it more

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

This is very helpful

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

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

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

this is very important note help me much more

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Hypothesis is a testable statement that explains what is happening or observed. It proposes the relation between the various participating variables. Hypothesis is also called Theory, Thesis, Guess, Assumption, or Suggestion. Hypothesis creates a structure that guides the search for knowledge.

In this article, we will learn what is hypothesis, its characteristics, types, and examples. We will also learn how hypothesis helps in scientific research.

What is Hypothesis?

A hypothesis is a suggested idea or plan that has little proof, meant to lead to more study. It’s mainly a smart guess or suggested answer to a problem that can be checked through study and trial. In science work, we make guesses called hypotheses to try and figure out what will happen in tests or watching. These are not sure things but rather ideas that can be proved or disproved based on real-life proofs. A good theory is clear and can be tested and found wrong if the proof doesn’t support it.

Hypothesis Meaning

A hypothesis is a proposed statement that is testable and is given for something that happens or observed.

• It is made using what we already know and have seen, and it’s the basis for scientific research.
• A clear guess tells us what we think will happen in an experiment or study.
• It’s a testable clue that can be proven true or wrong with real-life facts and checking it out carefully.
• It usually looks like a “if-then” rule, showing the expected cause and effect relationship between what’s being studied.

Characteristics of Hypothesis

Here are some key characteristics of a hypothesis:

• Testable: An idea (hypothesis) should be made so it can be tested and proven true through doing experiments or watching. It should show a clear connection between things.
• Specific: It needs to be easy and on target, talking about a certain part or connection between things in a study.
• Falsifiable: A good guess should be able to show it’s wrong. This means there must be a chance for proof or seeing something that goes against the guess.
• Logical and Rational: It should be based on things we know now or have seen, giving a reasonable reason that fits with what we already know.
• Predictive: A guess often tells what to expect from an experiment or observation. It gives a guide for what someone might see if the guess is right.
• Concise: It should be short and clear, showing the suggested link or explanation simply without extra confusion.
• Grounded in Research: A guess is usually made from before studies, ideas or watching things. It comes from a deep understanding of what is already known in that area.
• Flexible: A guess helps in the research but it needs to change or fix when new information comes up.
• Relevant: It should be related to the question or problem being studied, helping to direct what the research is about.
• Empirical: Hypotheses come from observations and can be tested using methods based on real-world experiences.

Sources of Hypothesis

Hypotheses can come from different places based on what you’re studying and the kind of research. Here are some common sources from which hypotheses may originate:

• Existing Theories: Often, guesses come from well-known science ideas. These ideas may show connections between things or occurrences that scientists can look into more.
• Observation and Experience: Watching something happen or having personal experiences can lead to guesses. We notice odd things or repeat events in everyday life and experiments. This can make us think of guesses called hypotheses.
• Previous Research: Using old studies or discoveries can help come up with new ideas. Scientists might try to expand or question current findings, making guesses that further study old results.
• Literature Review: Looking at books and research in a subject can help make guesses. Noticing missing parts or mismatches in previous studies might make researchers think up guesses to deal with these spots.
• Problem Statement or Research Question: Often, ideas come from questions or problems in the study. Making clear what needs to be looked into can help create ideas that tackle certain parts of the issue.
• Analogies or Comparisons: Making comparisons between similar things or finding connections from related areas can lead to theories. Understanding from other fields could create new guesses in a different situation.
• Hunches and Speculation: Sometimes, scientists might get a gut feeling or make guesses that help create ideas to test. Though these may not have proof at first, they can be a beginning for looking deeper.
• Technology and Innovations: New technology or tools might make guesses by letting us look at things that were hard to study before.
• Personal Interest and Curiosity: People’s curiosity and personal interests in a topic can help create guesses. Scientists could make guesses based on their own likes or love for a subject.

Types of Hypothesis

Here are some common types of hypotheses:

Simple Hypothesis

Complex hypothesis, directional hypothesis.

• Non-directional Hypothesis

Null Hypothesis (H0)

Alternative hypothesis (h1 or ha), statistical hypothesis, research hypothesis, associative hypothesis, causal hypothesis.

Simple Hypothesis guesses a connection between two things. It says that there is a connection or difference between variables, but it doesn’t tell us which way the relationship goes.

Complex Hypothesis tells us what will happen when more than two things are connected. It looks at how different things interact and may be linked together.

Directional Hypothesis says how one thing is related to another. For example, it guesses that one thing will help or hurt another thing.

Non-Directional Hypothesis

Non-Directional Hypothesis are the one that don’t say how the relationship between things will be. They just say that there is a connection, without telling which way it goes.

Null hypothesis is a statement that says there’s no connection or difference between different things. It implies that any seen impacts are because of luck or random changes in the information.

Alternative Hypothesis is different from the null hypothesis and shows that there’s a big connection or gap between variables. Scientists want to say no to the null hypothesis and choose the alternative one.

Statistical Hypotheis are used in math testing and include making ideas about what groups or bits of them look like. You aim to get information or test certain things using these top-level, common words only.

Research Hypothesis comes from the research question and tells what link is expected between things or factors. It leads the study and chooses where to look more closely.

Associative Hypotheis guesses that there is a link or connection between things without really saying it caused them. It means that when one thing changes, it is connected to another thing changing.

Causal Hypothesis are different from other ideas because they say that one thing causes another. This means there’s a cause and effect relationship between variables involved in the situation. They say that when one thing changes, it directly makes another thing change.

Hypothesis Examples

Following are the examples of hypotheses based on their types:

Simple Hypothesis Example

• Studying more can help you do better on tests.
• Getting more sun makes people have higher amounts of vitamin D.

Complex Hypothesis Example

• How rich you are, how easy it is to get education and healthcare greatly affects the number of years people live.
• A new medicine’s success relies on the amount used, how old a person is who takes it and their genes.

Directional Hypothesis Example

• Drinking more sweet drinks is linked to a higher body weight score.
• Too much stress makes people less productive at work.

Non-directional Hypothesis Example

• Drinking caffeine can affect how well you sleep.
• People often like different kinds of music based on their gender.
• The average test scores of Group A and Group B are not much different.
• There is no connection between using a certain fertilizer and how much it helps crops grow.

Alternative Hypothesis (Ha)

• Patients on Diet A have much different cholesterol levels than those following Diet B.
• Exposure to a certain type of light can change how plants grow compared to normal sunlight.
• The average smarts score of kids in a certain school area is 100.
• The usual time it takes to finish a job using Method A is the same as with Method B.
• Having more kids go to early learning classes helps them do better in school when they get older.
• Using specific ways of talking affects how much customers get involved in marketing activities.
• Regular exercise helps to lower the chances of heart disease.
• Going to school more can help people make more money.
• Playing violent video games makes teens more likely to act aggressively.
• Less clean air directly impacts breathing health in city populations.

Functions of Hypothesis

Hypotheses have many important jobs in the process of scientific research. Here are the key functions of hypotheses:

• Guiding Research: Hypotheses give a clear and exact way for research. They act like guides, showing the predicted connections or results that scientists want to study.
• Formulating Research Questions: Research questions often create guesses. They assist in changing big questions into particular, checkable things. They guide what the study should be focused on.
• Setting Clear Objectives: Hypotheses set the goals of a study by saying what connections between variables should be found. They set the targets that scientists try to reach with their studies.
• Testing Predictions: Theories guess what will happen in experiments or observations. By doing tests in a planned way, scientists can check if what they see matches the guesses made by their ideas.
• Providing Structure: Theories give structure to the study process by arranging thoughts and ideas. They aid scientists in thinking about connections between things and plan experiments to match.
• Focusing Investigations: Hypotheses help scientists focus on certain parts of their study question by clearly saying what they expect links or results to be. This focus makes the study work better.
• Facilitating Communication: Theories help scientists talk to each other effectively. Clearly made guesses help scientists to tell others what they plan, how they will do it and the results expected. This explains things well with colleagues in a wide range of audiences.
• Generating Testable Statements: A good guess can be checked, which means it can be looked at carefully or tested by doing experiments. This feature makes sure that guesses add to the real information used in science knowledge.
• Promoting Objectivity: Guesses give a clear reason for study that helps guide the process while reducing personal bias. They motivate scientists to use facts and data as proofs or disprovals for their proposed answers.
• Driving Scientific Progress: Making, trying out and adjusting ideas is a cycle. Even if a guess is proven right or wrong, the information learned helps to grow knowledge in one specific area.

How Hypothesis help in Scientific Research?

Researchers use hypotheses to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method:

• Initiating Investigations: Hypotheses are the beginning of science research. They come from watching, knowing what’s already known or asking questions. This makes scientists make certain explanations that need to be checked with tests.
• Formulating Research Questions: Ideas usually come from bigger questions in study. They help scientists make these questions more exact and testable, guiding the study’s main point.
• Setting Clear Objectives: Hypotheses set the goals of a study by stating what we think will happen between different things. They set the goals that scientists want to reach by doing their studies.
• Designing Experiments and Studies: Assumptions help plan experiments and watchful studies. They assist scientists in knowing what factors to measure, the techniques they will use and gather data for a proposed reason.
• Testing Predictions: Ideas guess what will happen in experiments or observations. By checking these guesses carefully, scientists can see if the seen results match up with what was predicted in each hypothesis.
• Analysis and Interpretation of Data: Hypotheses give us a way to study and make sense of information. Researchers look at what they found and see if it matches the guesses made in their theories. They decide if the proof backs up or disagrees with these suggested reasons why things are happening as expected.
• Encouraging Objectivity: Hypotheses help make things fair by making sure scientists use facts and information to either agree or disagree with their suggested reasons. They lessen personal preferences by needing proof from experience.
• Iterative Process: People either agree or disagree with guesses, but they still help the ongoing process of science. Findings from testing ideas make us ask new questions, improve those ideas and do more tests. It keeps going on in the work of science to keep learning things.

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Summary – Hypothesis

A hypothesis is a testable statement serving as an initial explanation for phenomena, based on observations, theories, or existing knowledge. It acts as a guiding light for scientific research, proposing potential relationships between variables that can be empirically tested through experiments and observations. The hypothesis must be specific, testable, falsifiable, and grounded in prior research or observation, laying out a predictive, if-then scenario that details a cause-and-effect relationship. It originates from various sources including existing theories, observations, previous research, and even personal curiosity, leading to different types, such as simple, complex, directional, non-directional, null, and alternative hypotheses, each serving distinct roles in research methodology. The hypothesis not only guides the research process by shaping objectives and designing experiments but also facilitates objective analysis and interpretation of data, ultimately driving scientific progress through a cycle of testing, validation, and refinement.

FAQs on Hypothesis

What is a hypothesis.

A guess is a possible explanation or forecast that can be checked by doing research and experiments.

What are Components of a Hypothesis?

The components of a Hypothesis are Independent Variable, Dependent Variable, Relationship between Variables, Directionality etc.

What makes a Good Hypothesis?

Testability, Falsifiability, Clarity and Precision, Relevance are some parameters that makes a Good Hypothesis

Can a Hypothesis be Proven True?

You cannot prove conclusively that most hypotheses are true because it’s generally impossible to examine all possible cases for exceptions that would disprove them.

How are Hypotheses Tested?

Hypothesis testing is used to assess the plausibility of a hypothesis by using sample data

Can Hypotheses change during Research?

Yes, you can change or improve your ideas based on new information discovered during the research process.

What is the Role of a Hypothesis in Scientific Research?

Hypotheses are used to support scientific research and bring about advancements in knowledge.

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What Does Nvidia’s Stock Split Mean for Investors?

The semiconductor giant’s stock will carry a fair value estimate of $105 after its 10-for-1 split.

Semiconductor firm Nvidia NVDA announced a 10-for-1 stock split along with its blowout first-quarter earnings results on Wednesday. The stock split means investors will receive nine additional shares for each one they already own.

“The split is reasonable since the stock price has appreciated so significantly,” says Morningstar technology equity strategist Brian Colello .

Nvidia shares are up more than 90% this year and more than 200% over the past 12 months, as the company has boomed thanks to the key role its semiconductor chips play in training and running artificial intelligence models. It now trades at over $1,000 per share, while it went for $495 at the end of 2023. The stock was changing hands near $305 per share in May 2023, just before the firm reported blowout earnings that kicked off the AI stock frenzy.

The firm’s last stock split was in July 2021, when it issued three new shares for every one outstanding (a four-for-one split).

The Date for Nvidia’s Stock Split

According to the company’s press release , the split is slated to occur after the stock market’s close on June 7. Shares will trade on a post-split basis starting June 10.

Nvidia Stock Price

Colello raised his fair value estimate for Nvidia stock from $910 to $1,050 following the company’s first-quarter results, which saw revenue of $26 billion—an 18% increase over the previous quarter and a 262% increase over the year-ago quarter.

What Nvidia’s Stock Split Means

While the split will increase the number of outstanding shares in circulation, it will not change the company’s overall value or affect Morningstar’s view of its stock. “Splitting the stock shouldn’t create economic value in theory, but it will make the company more accessible to smaller investors,” Colello explains. While $500 isn’t enough to buy a single share of Nvidia today, he explains, it will be enough to buy several shares after the split.

After the split, Nvidia’s fair value estimate will be adjusted to $105. The firm’s wide economic moat rating will be unaffected, as will its 3-star rating (meaning the stock is considered fairly valued) and very high uncertainty rating.

Nvidia’s AI Boom

The firm’s first-quarter earnings show it “remains the clear winner in the race to build out generative artificial intelligence capabilities,” Colello writes. “We’re encouraged by management’s commentary that demand for its upcoming Blackwell products should exceed supply into calendar 2025, and we see no signs of AI demand slowing either.”

Colello is looking ahead to strong revenue growth from data centers over the next several quarters, and he expects additional growth from a higher installed base of AI equipment. He is anticipating revenue of $29.7 billion in the next quarter—slightly more than Nvidia’s estimate.

Colello doesn’t believe the rush of companies buying Nvidia’s chips will stall—for now, at least. He says the firm’s production is still well-matched to customer demand, though the risk bears watching. “Given Nvidia’s astronomical growth, we continue to assess the risk of companies buying too many AI GPUs too soon, leading to an air pocket and excess inventory at some point in the future. We see no such signs today,” he writes.

Why Do Companies Split Their Stock?

When a company splits its stock, each share gets divided into multiple new shares. While this increases the number of outstanding shares, it does not change the company’s overall value (its market capitalization). Firms tend to do this when their share price has risen dramatically to an amount that might make it difficult for individual investors to purchase them. Having a larger number of cheaper shares to attract more buyers can help improve liquidity, and lower prices can also have the psychological impact of making shares look more attractive to investors, even though the company’s underlying value hasn’t changed.

Other Recent Stock Splits

Nvidia isn’t the only major company to split its shares in recent years. Retail giant Walmart WMT enacted a 3-for-1 split in February, while Alphabet GOOGL / GOOG , Tesla TSLA , and Amazon AMZN split shares in 2022.

The author or authors do not own shares in any securities mentioned in this article. Find out about Morningstar’s editorial policies .

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What is pasteurization? A dairy expert explains how it protects against foodborne illness, including avian flu

Associate Research Professor of Food Science, Penn State

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Kerry Elizabeth Kaylegian receives funding from USDA.

Penn State provides funding as a founding partner of The Conversation US.

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Recent reports that the H5N1 avian flu virus has been found in cow’s milk have raised questions about whether the U.S. milk supply is safe to drink. According to the federal Food and Drug Administration, the answer is yes, as long as the milk is pasteurized .

Nonetheless, raw (unpasteurized) milk sales are up , despite health experts’ warning that raw milk could contain high levels of the virus , along with many other pathogens.

As an extension food scientist in a state where raw milk sales are legal , I provide technical support to help processors produce high-quality, safe dairy foods. I also like to help people understand the confusing world of pasteurization methods on their milk labels, and why experts strongly discourage consuming raw milk and products made from it.

What can make milk unsafe

Dairy products, like many foods, have inherent risks that can cause a variety of illnesses and even death. Our milk comes from animals that graze outdoors and live in barns. Milk is picked up from the farm in tanker trucks and delivered to the processing plant. These environments offer numerous opportunities for contamination by pathogens that cause illness and organisms that make food spoil.

For example, listeria monocytogenes comes from environmental sources like soil and water. Mild infections with listeriosis cause flu-like symptoms. More serious cases are, unfortunately, too common and can cause miscarriages in pregnant women and even death in extreme cases .

Other pathogens commonly associated with dairy animals and raw milk include E. coli , which can cause severe gastrointestinal infections and may lead to kidney damage; Campylobacter , the most common cause of diarrheal illness in the U.S.; and Salmonella , which cause abdominal pain, diarrhea and other symptoms.

Keeping beverages safe with heat

In the 1860s, French microbiologist Louis Pasteur discovered that heating wine and beer killed the organisms that caused spoilage, which then was a significant problem in France.

This heating process, which became known as pasteurization, was adopted in the U.S. prior to World War II, at a time when milk was responsible for 25% of all U.S. outbreaks of foodborne illnesses . In 1973, the federal government required that all milk sold across state lines in the U.S. had to be pasteurized, and in 1987, it banned interstate sales of raw milk .

Pasteurization heats every particle of a food to a specific temperature for a continuous length of time in order to kill the most heat-resistant pathogen associated with that product. Different organisms have different responses to heat, so controlled scientific studies are required to determine what length of time at a given temperature will kill a specific organism.

Since 1924, pasteurization in the U.S. has been guided by the Grade “A” Pasteurized Milk Ordinance , a federal guidance document that is updated every two years to reflect current science and has been adopted by all 50 states . Pasteurization equipment in the U.S. must meet stringent requirements that include sanitary design, safety controls and material standards.

Pasteurization methods

Dairy processors can choose among several different types of pasteurization. When executed properly, all of these methods produce the same result: pathogen-free milk. Processors may treat milk beyond minimum times or temperatures to provide an extra margin of safety, or to reduce bacteria that can cause milk to spoil, thus increasing the product’s shelf life.

Vat pasteurizers , also known as batch pasteurizers, often are used by smaller-scale processors who handle limited volumes. The milk is pumped into a temperature-controlled tank with a stirrer, heated to a minimum of 145 degrees Fahrenheit (63 Celsius) and held there continuously for 30 minutes. Then it is cooled and pumped out of the vat.

The most common method used for commercial milk is high-temperature short-time pasteurization , which can treat large volumes of milk. The milk is pumped through a series of thin plates at high speed to reach a minimum temperature of 161 F (71 C). Then it travels through a holding tube for 15 seconds, and the temperature is checked automatically for safety and cooled.

The most complex and expensive systems are ultra-pasteurizers and ultra-high-temperature pasteurizers , which pasteurize milk in just a few seconds at temperatures above 285 F (140 C). This approach destroys many spoilage organisms, giving the milk a significantly longer shelf life than with other methods, although sometimes products made this way have more of a “cooked” flavor.

Ultra-high-temperature products are processed in a sterile environment and packaged in sterile packaging, such as lined cartons and pouches. They can be shelf-stable for up to a year before they are opened. Ultra-high-temperature packaging makes taking milk to school for lunch safe for kids every day.

Avian flu in milk

The detection of avian flu virus fragments in milk is a new challenge for the dairy industry. Scientists do not have a full picture of the risks to humans but are learning.

Research so far has shown that virus particles end up in the milk of infected cows, but that pasteurization will inactivate the virus . However, the FDA is advising consumers not to drink raw milk because there is limited information about whether it may transmit avian flu .

The agency also is urging producers not to manufacture or sell raw milk or raw milk products, including cheese, made with milk from cows showing symptoms of illness.

It’s never a good time to get a foodborne illness, and this is the beginning of ice cream season . At a time when avian flu is showing up in new species and scientists are still learning about how it is transmitted, I agree with the FDA that raw milk poses risks not worth taking.

• Avian flu H5N1
• Food safety
• Dairy products
• US agriculture
• Significant Terms
• Foodborne illness

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