Business
A/B Test Hypothesis Generator
An A/B test hypothesis generator structures an experiment so the result is trustworthy rather than a coin flip dressed up as data. Enter the change to test and your primary metric, and it returns a hypothesis in the "because [evidence], we believe [change] will [effect]" format, plus control and variant definitions, guardrail metrics, sample size and duration prompts, and an explicit decision rule. Product managers, marketers, and growth teams use it to avoid common A/B mistakes — testing several things at once, stopping early when a result looks promising, or having no pre-agreed decision rule. A clean test changes one variable, runs for full business cycles, and is decided by a rule set in advance. Ground the hypothesis in a real observation and commit to the rule before you run.
How to use
- Choose your options above
- Click Generate
- Copy your result
Detailed instructions
- Enter the change to test and the primary metric.
- Click Generate to produce the hypothesis and plan.
- Set sample size, duration, and guardrails.
- Commit to the decision rule, then launch the test.
Use Cases
- •Writing a rigorous A/B test hypothesis
- •Defining control, variant, and one primary metric
- •Setting guardrail metrics that must not regress
- •Agreeing a decision rule before launching a test
- •Avoiding peeking and early-stopping mistakes
Tips
- →Ground the hypothesis in a real observation.
- →Change one variable at a time.
- →Run for full business cycles and do not peek.
- →Agree the decision rule before you launch.
FAQ
What is a good A/B test hypothesis format?
"Because [evidence], we believe [change] will [effect] for [audience], and we will know when we see [result] at significance." Grounding it in a real observation and a measurable expected outcome keeps the test purposeful and falsifiable.
Why only change one variable at a time?
If the variant differs in several ways, a winning result tells you nothing about which change caused it. Testing one variable at a time keeps the learning clean, even though it means running more tests overall.
Why set the decision rule before launching?
Deciding the significance threshold and guardrails in advance stops anyone from stopping early on a lucky swing or rationalising a weak result. Pre-committing to the rule is what makes the test a real decision tool rather than theatre.
What are guardrail metrics?
Metrics that must not get worse even if the primary metric improves — for example, revenue or quality alongside a conversion rate test. They prevent shipping a "winning" variant that silently damages something else important.
How do I determine sample size and test duration?
Sample size depends on your baseline conversion rate, the minimum detectable effect you care about, and the statistical power you want (typically 80%+). Duration should cover at least one or two full business cycles — do not stop early when results look promising.
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