Free AI Hypothesis Generator (Build Testable Hypotheses)
If your A/B tests aren't giving you useful results, the test itself might not be the problem. Sometimes, nobody writes a hypothesis before running it. They changed a button, rewrote a headline, and maybe moved some stuff around. But there was no prediction. No metric attached. So when the results came back flat, there was nothing to learn from.
A hypothesis gives your experiment a point. You're basically saying, "If we change X, then Y will happen, and here's how we'll know." That's what turns a random test into a useful one, whether you're tweaking one page or running a research process across your whole site.
This free AI hypothesis generator handles that setup. Describe what's going on, and it builds a concise, well-structured hypothesis you can take straight into testing.
What is a hypothesis in experimentation?
A hypothesis is your best guess in a form that the data can prove wrong. In A/B testing, that means naming the change, the behavior you expect to move, and the number you'll use to judge the result.
Here's a quick example. Say your checkout page has a 62% abandonment rate, and your data shows users drop off when shipping costs appear. A structured hypothesis would look like this:
"We have observed that 62% of users abandon their cart at the shipping cost step. By analyzing checkout flow data. We wish to display shipping costs earlier in the process. For the segment of users who add items but leave at payment. This will lead to increased checkout completion, measured by cart abandonment rate and conversion rate."
See how that forces clarity? You're defining the research context, identifying variables, setting a measurable outcome, and giving the test a direction. Without that, you're just guessing.
A hunch might say, "Let's try green buttons," while a hypothesis says, "Changing the CTA to a higher-contrast color will increase click-through rates for mobile users by 8%, because heatmap data shows low visual engagement on the current design."
The difference sounds obvious. One gives you ideas. And the other gives you something you can actually evaluate.
What this hypothesis generator does
Describe your situation in the text field, what's happening, what you've noticed, what you think needs to change, and the tool turns that into a structured hypothesis you can use right away. Here's what it handles:
Observation framing: It pulls the core problem from your research context and states it clearly. Your test starts from existing data, not assumptions.
Change definition: It specifies the independent variable you're modifying, so the experiment stays focused.
Audience segmentation: It identifies which user segment you're targeting. A test aimed at "everyone" rarely tells you anything useful.
Outcome prediction: It generates a measurable expected outcome tied to specific metrics, like conversion rate, retention, or revenue per session.
Measurement criteria: It outlines how you'll evaluate success, so your team shares the same objectives before the experiment starts.
The output is concise and built to enhance your testing process. Review it, challenge it, and take it into your next experiment.
How to write a hypothesis (and when to use this tool)
Writing one yourself isn't hard. You just need to follow a clear process.
Step 1. Start with data, not a feeling
Pull up your analytics, heatmaps, session recordings, whatever you have. Look for a real problem. Something like "our bounce rate is high" is too vague to act on. But "74% of mobile visitors leave the landing page within 5 seconds"? That's input data you can actually build a hypothesis around. The richer your research context, the sharper your hypothesis.
Step 2. Pick one variable to change
The headline. The CTA placement. The number of form fields. How visible your social proof is. It doesn't matter which one, just pick one. If you test multiple variables at the same time, you won't know which one caused the outcome. The results won't make sense.
Step 3. Define who you're targeting
Returning customers behave differently than first-time visitors. So be specific about your segment. Are you looking at "new users arriving from paid search"? Or "mobile shoppers who already have items in cart"?
Narrowing this down improves the feasibility of your test and makes it easier to predict how the change will affect user behavior.
Step 4. State what you expect to happen
"More conversions" is too vague. Say something like "a 12% increase in add-to-cart rate" instead. That gives you a benchmark to test against. Your outcome should be measurable and tied to a metric your team already tracks.
Step 5. Decide how you'll measure it
What metric will decide success, and what level of statistical significance will you accept? Set the end condition upfront, too, either a fixed run time or a minimum sample size. If you skip this, teams sometimes stop early or read random variation as a win, which doesn't make sense when you're trying to learn something.
Now, if doing all five steps for every test sounds like a lot, that's where this hypothesis generator comes in. Drop in a few sentences about your situation and let the tool handle the structure. You still review the output and make it yours, but the foundation shows up in seconds so you can begin testing sooner.
Who should use this hypothesis maker tool?
Anyone running experiments or working with data can get value from this, but these are the ones who tend to use it most.
CRO and experimentation teams
You're running multiple experiments across landing pages, checkout flows, and product pages every month. Manually writing testable hypotheses for each one eats up time you'd rather spend on analysis.
This tool gives you a starting point to refine and plug into your testing workflow. Need to generate multiple hypotheses quickly and prioritize them with ICE or PIE? That's exactly what it's for.
Product managers
You're making feature decisions based on research questions and user feedback. A structured hypothesis reframes product changes as experiments instead of opinions. That makes it easier to get buy-in during product development, and easier to evaluate whether a change actually improved your website's performance.
Marketing teams
You're testing ad copy, landing pages, email subject lines, and offer placements. Maybe you're reacting to market trends or launching something new. Every test benefits from a clear prediction about consumer behavior, from ad exposure to email open rates. This tool gets you to that prediction fast, so you spend time on execution and data collection instead of staring at a blank doc.
Students and researchers
Working on research projects, academic papers, or just learning scientific methodology? A hypothesis maker that helps you develop structured predictions is a solid way to practice critical thinking and understand the relationship between variables and outcomes.
Use the generated hypothesis as a starting point, then review it against existing research and your specific methodology.
Agencies managing client experiments
Every client comes with different research questions, data, and goals. You need to create well-structured hypotheses at scale. This tool cuts formulation time so you can focus resources on delivering insights that actually move the needle.
Tips for writing better hypotheses
The tool does the structuring for you, but the quality of the output depends on how you think about the problem going in. Whether you're running a quick A/B test or something more scientific, these tips help with that.
Ground every hypothesis in data, not opinion: If you can't point to a real data source behind your observation, whether that's analytics, user research, or insights from existing research, what you have is a guess. Guesses are fine for brainstorming. They're not fine for running experiments.
Make it falsifiable: This is the one people skip. If there's no outcome that could prove you wrong, it's not a testable hypothesis. You need a pass/fail condition tied to measurable metrics. Otherwise, you'll end up rationalizing whatever result you get.
Stay focused: One change. One segment. One predicted outcome. Resist the urge to cram everything into a single test, because when you do, you can't tell what caused what. If you've got multiple ideas, generate alternative hypotheses for each and then prioritize.
Put a timeframe on it: "This will improve conversions" doesn't give you anything to hold yourself to. "This will improve conversions by 10% within the first month" does. Time-bound predictions keep your experiments from dragging on without a clear endpoint.
Review before you launch: Treat the generated hypothesis as a brief, not something final. Have your team push back on it. Does the logic hold? Is the metric actually the right one? Could there be an alternative explanation for the predicted outcome you haven't thought of? That kind of critical thinking is what separates a hypothesis that teaches you something from one that just confirms what you already believed.