Free Hypothesis Generator
Finding it hard to come up with A/B test ideas that actually move the needle?
Test smarter with Fibr’s AI-powered Hypothesis Generator. Optimize conversions, engagement, and retention with structured hypotheses, and run your next A/B tests with confidence.
What is a Hypothesis in A/B Testing?
A hypothesis, in the context of A/B testing is a clear, testable prediction about how a specific change to your website will affect user behavior and other metrics.
It's basically a structured statement that outlines what you're changing, why you're changing it, and what outcome you expect.
For example, rather than simply testing different button colors, a hypothesis would state Changing the checkout button from grey to orange will increase click-through rates because it creates more visual contrast and urgency.
Why Do You Need to Have a Hypothesis?
When you conduct A/B tests without a hypothesis, you might move forward, but you won't know if you're heading in the right direction.
A hypothesis concretizes your testing plan
A properly formed hypothesis keeps your testing program focused and measurable. It helps you understand not just what worked or didn't work, but why, so that you can build a knowledge base of insights that will guide future optimizations.
It lends a stronger base for your experiments
Having a hypothesis also prevents the common pitfall of running tests based on hunches or personal preferences. It forces you to think critically about your changes and their potential impact on user behavior.
It teaches you how to gradually build better tests
It is a structured process; it helps you prioritize tests that have the highest potential impact on your conversion goals. In the process, you learn from both successful and unsuccessful experiments.
Steps to Use Fibr’s Hypothesis Generator
Fibr’s tool is simple and it takes less than a minute to generate a hypothesis once you have a brief. Here’s how you use it:
Step 1: Find out what you want from your test
First you need to have an idea of what you want to achieve with your A/B test. Goals are usually like increasing conversions, reducing bounces, better email open rates, etc.
A clear goal keeps your hypothesis relevant and measurable.
Step 2: Input key metrics and variables
The simple hypothesis generation tool needs some input data in the brief you need to write. Your hypothesis comes out stronger when you have data like:
Your current site performance, like your average session time, conversion rates, etc.
CTRs, heatmaps, and other info on user behavior
Testing variables, like your CTA color, headline text, page layout.
Step 3: Choose a hypothesis framework
A structured hypothesis should include:
Change (independent variable): What are you modifying?
Expected impact (dependent variable): What metric do you expect to change?
Rationale: Why do you believe this change will have an impact?
A common format is: If we [make a change], then [expected result] because [reason based on data].
In practice, the tool will generate a hypothesis like this: If we change the CTA button from blue to orange, then click-through rates will increase because previous tests show warm colors attract more attention.
Step 4: Review and refine hypothesis suggestions
The tool will generate multiple hypotheses. Evaluate them based on their feasibility, impact and your supporting data. Ask yourself these questions:
Can I implement and test this change easily?
Will this test provide meaningful insights?
Does existing data validate the assumption?
You may need to tweak the hypotheses or combine ideas to form stronger test candidates.
Step 5: Prioritize hypotheses
Not all hypotheses should be tested at once. Use prioritization frameworks like ICE score (Impact, Confidence, Ease) and the PIE framework (Potential, Importance, Ease).
For example, if a hypothesis has high impact and is easy to implement, it should be tested first.
Step 6: Implement and test
Once you finalize a hypothesis, move to execution.
Set up the A/B test using an experimentation platform. We recommend Fibr as it’s the most feature-loaded and cost effective (Free, actually!) A/B testing platform out there.
Monitor your A/B test for statistical significance before drawing conclusions. (Fibr handles that too!)
Use Fibr’s Free Hypothesis Maker Today
The benefits are clear, a hypothesis generator simplifies the A/B testing process and ensures that tests are structured, data-driven, and impactful.
However, the tool is just a starting point—you still need critical thinking and validation to run meaningful experiments.
Get your hypotheses in minutes with Fibr’s free tool.
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