How To Use Hypothesis Testing to Drive Revenue

Too many business decisions hinge on habits, instinct, or the loudest person in the room. 

Hypothesis testing offers a better way.

Instead of working off on untested assumptions, you can run low-risk experiments and guide your next move based on real-world data. Cut waste, reduce risk, and uncover what actually drives revenue.

What is Hypothesis Testing?

Hypothesis testing is a statistical method for using sample data to make confident decisions about a larger population. There are many types of hypothesis testing and ways to gather data, but they all follow the same core process:

• Start with an assumption (hypothesis) about a population
• Collect sample data that’s representative of the population
• Use statistical tests to evaluate how plausible the assumption is

Each step requires care to ensure the results reflect the real world. The sample must accurately represent the broader population, confounding variables need to be controlled, and the test design must match the question being asked.

Done right, this process lets you validate assumptions about a large population using a relatively small sample, and gives you a high degree of confidence in your findings.

Hypothesis testing for business

Brands are very interested in the particular populations that make up their target market: who are they in terms of age, occupation, and income? What do they like, fear, desire, and imagine for themselves? 

Marketing, sales, and business strategy are always bound to the assumptions that brands make about their market. Hypothesis testing is a way to see which of those beliefs actually hold water in the real world.

In business, this usually involves studying how a small group of users or customers respond to a change, comparing those results with a control group, and then using the results to decide what to do next. 

Hypothesis testing is often used for evaluating the impact of new messaging, strategies, features, or design changes. But it can also be used to optimize incentive structures for employees, assess the viability of entering a new geographic region, or test a new pricing model.

How much math do I need to know for hypothesis testing?

Scientists who are qualified to design randomized controlled trials get paid hundreds of thousands of dollars a year. In academia, researchers secure millions of dollars in funding to test hypotheses based on their experimental design.

If you’re not a statistician (and most marketers aren’t), the best move is to pick a platform that handles test setup, group assignment, and statistical analysis. These tools help you:

• Avoid biased test structures
• Choose the right method automatically
• See clearly whether your changes worked

With A/B testing tools, for example, you only need a website with traffic to start running tests. The platform handles all the randomization and statistical analysis, which allows founders, marketers, and website owners to focus on the strategy.

There is definitely an art to A/B testing that takes time to learn, but the math and stats are not what holds people back.

Hypothesis testing is finding a lever

In practical terms, hypothesis testing is how you discover levers: actions that reliably drive outcomes. 

You might believe that promos outperform educational content. Or that new customers respond better to social proof than urgency. 

With testing, you challenge your own idea. You ask the data to prove you wrong. If it can’t, you’ve found a lever that is worth pulling again.

How To Run Hypothesis Testing

This is a simple walkthrough of the basic steps necessary to set up and run hypothesis testing for business. 

If you want to learn more about the concepts or math covered here, I highly recommend YouTube for quick knowledge, especially the content aimed at statistics students. With more time, I’d explore online courses, like Fundamentals of Statistics from MITx. 

Step 1: Define your business problem and identify variables

What problem are you trying to solve? What question are you trying to answer?

Before you start creating a hypothesis, clearly articulate the business problem and identify the variables that you want to test. Here’s a few examples:

• Will a new offer improve the conversion rate?
• Will a less labor-intensive website design impact sales?
• Will revamping our sales/onboarding process decrease churn?
• Will a new bonus structure increase sales velocity and/or deal size?
• Will removing the credit card requirement for free trials decrease customer lifetime value?

Looking at these examples, you find that each includes two clearly identified variables:

• Independent variable: what you’re changing, such as the offer..
• Dependent variable: what you’re measuring, such as the conversion rate.

It’s important to define these early because you need to know which data sources are going to be important to track. For quick A/B tests, this is easy, but once you start measuring churn, lifetime value, or customer satisfaction, there’s a lot more to think about.

I also want to point out that at this stage, you want to be 100% sure that the variables are tied to measurable goals that contribute to revenue — sales, conversions, operational costs, churn, and so on.

If you are using hypothesis testing in academic research or a pure R&D setting, this may not be true. In business, though, you need to ensure that successful experiments will directly contribute to positive business outcomes. 

Step 2: Formulate your hypotheses

Now that you have a clearly defined business problem and variables, you can formulate the two hypotheses necessary to run a test. These are:

• Null hypothesis: Represents the status quo, no effect. For example, “The new onboarding process has no impact on churn”
• Research (or alternate) hypothesis: Represents the effect you expect to see. For example, “The new onboarding process reduces churn by at least 4%”

Hypothesis testing begins by assuming the null is true. Your goal is to collect enough evidence to confidently reject it in favor of the alternative. 

It may seem backward, but this structure helps guard against false positives, and ensures that any observed effect is real and not just random noise.

The null hypothesis becomes your baseline: the assumption that a new subject line, price point, or landing page performs no differently than what you’re already using.

Step 3: Select your test design and analysis

Once you’ve defined your hypotheses, the next step is to choose a test design that can evaluate them accurately. It has to fit your variables, timeline, budget, and the realities of your business.

Your options fall into three basic categories:

• Randomized controlled trials (RCTs) deliver the strongest results. You split users randomly and test different versions of websites, ads, or emails. Random assignment eliminates bias and proves your changes actually caused any differences you find.
• Quasi-experimental designs are structured like an experiment but lack true randomization, which isn’t always possible. You might test a feature in one city while comparing results to another city. That’s a quasi-experiment. Be aware it’s easier for biases and confounding variables to distort your results, but sometimes this is the best viable option.
• Observational studies skip the experiment entirely. You simply analyze what’s already happened. These can surface interesting patterns, like seeing that mobile users tend to spend more, but they don’t prove that one thing caused the other. 

Each of these approaches comes with tradeoffs. A/B testing is an easy way to run an RCT, but it isn’t always viable, and you might need to look at quasi-experimental designs to help you get the best possible data.

You also have to select your analysis methods. Three of the most common analysis options are:

• T-tests
• Chi-square tests
• ANOVA (Analysis of Variance)

You need to decide on your design and analysis before you collect data. It’s very easy for trained researchers to manipulate the analysis and show an effect where there isn’t one (Google p-hacking, if you are curious). Novice researchers can make similar mistakes unwittingly.

Step 4: Set Up Your Experiment

Once you’ve chosen your test design, you need to define the key inputs that shape how the test runs and how its results are interpreted. Like your design and analysis, you cannot tweak these inputs down the line. They need to be locked in before data collection begins.

Start with your significance level (α), or alpha. This represents the level at which you are willing to reject a null hypothesis that’s actually true. In many cases, researchers use a significance level of 5% (α = 0.05), though stricter levels (α = 0.02) can be used in high-stakes situations, and looser levels (α = 0.10) can be used for lower-stakes, exploratory questions. 

Practically speaking, significance level defines the threshold for how confident you want to be in your results. The lower the alpha, the more confident you can be in the results.

Next, choose your statistical power, which is typically set at 80% or 90%. This tells you how likely your test is to detect a real effect if one exists. 

Finally, estimate your expected effect size. This is how big of a difference you expect to see between versions.

Once you have these inputs, you’re ready to calculate the sample size you need using power analysis. This is a method researchers use to design studies that are large enough to detect real effects, but not so large as to waste resources. Keep in mind that:

• A lower significance level (more confidence) requires more data.
• A higher statistical power (less risk of false negatives) requires more data.
• A smaller expected effect size requires more data to detect.

In practice, you won’t need to crunch these numbers by hand. Use a sample size calculator or a testing tool that does it for you. Most will ask for the three inputs above and then tell you how many users (or sessions, conversions, etc.) you need in each group before you can call the test.

Step 5: Run the test

Once your test is live, your main job is to leave it alone. That means no peeking at early results and no stopping the test early just because one version is ahead.

Test duration is an important factor to consider. You want to run the test long enough to capture a full cycle of customer behavior. I would say that one full business week is the bare minimum. 

On a website, for example, you might have a test where you hit the required sample size in less than a week. Pulling it then might skew the results or miss important behavioral patterns that you would have picked up over the full cycle.

Step 6: Interpret and act

Once the test is over, it’s time to figure out what the results actually mean. Most testing tools will give you three key outputs

• The p-value tells you the probability of seeing your result (or something more extreme) if there were actually no difference between versions.Typically, if it’s below your significance level, the result is considered meaningful.
• The confidence interval shows the likely range of the true effect. Narrow intervals are more reliable; wide ones mean more uncertainty.
• The effect size tells you how big the impact was. Even if a result is statistically significant, a tiny effect may not be worth acting on.

When interpreting results, look for alignment across all three outputs. A low p-value with a wide confidence interval and tiny effect size may not be worth acting on. 

Perhaps you get a winner but notice some side-effects that threaten other metrics you care about. For example, say a company pilots a successful referral program (it increases signups) but the referral cohorts have really high churn? It’s not as clear cut.

When results are borderline or unclear, consider running the test again with a larger sample or testing a more dramatic variation. 

And always: sanity-check the outcome against real-world behavior and common sense before rolling out changes. Does this outcome make sense? Is it likely to work in other contexts?

Hypothesis Testing and Generating Revenue: 7 Examples

Let’s look at real-world examples where businesses use it to improve margins, reduce waste, and increase ROI.

1. Deploy more cost-efficient designs

Not every high-production design drives better results, and they can easily drain your budget over time. Test a simplified version of a high-cost asset (e.g. a homepage layout that’s easier to maintain, a basic mailer insert, or stripped-down packaging) and track whether engagement or conversion drops. If performance holds steady, you’ve uncovered a durable cost-saving opportunity.

2. Evaluate ad placement

Where your ads run can matter more than how they look. Use unique QR codes, URLs, or phone numbers to compare performance across placements like trade publications, billboards, and digital advertising channels.

3. Test retargeting strategies

Discounts, free shipping, and other incentives work well, but sometimes nurturing can work even better. Test educational or social-proof-based retargeting ads against aggressive discount-based ones to see which drives conversions and lifetime value higher.

4. Experiment with checkout nudges

You can use subtle psychological cues to boost urgency and trust at checkout. Try adding real-time stock counts (“only 3 left”), social proof (“just bought by Clara in Boston”), or reassurance (“free returns”). Measure to see if there is a positive impact on cart completion or average order value.

5. Optimize cold outreach: 

Experiment at the very top of your sales funnel to find out what opens the most doors. Send one set of prospects a pain-focused message and another a gain-focused message, then find your strongest angle by comparing open, reply, and meeting rates.

6. Assess influencer performance

Big influencers often bring vanity metrics; smaller creators may deliver better ROI. Compare performance between nano- and micro-influencers (under 10K vs. 50–100K followers) using unique tracking links to measure engagement and conversion.

7. Concentrate your email cadence

Is more than one email a week really worth it? Send one high-value email per week to a test group while keeping the usual flow for your control. Maybe you can get the same results with less work for you, and less email fatigue for your subscribers.