Convert a product idea into a falsifiable A/B test hypothesis with a metric, direction, population, and decision threshold.
A hypothesis is the contract that makes the eventual p-value interpretable. A usable A/B test starts with a hypothesis, not a variant. In statistical language, you are comparing a null hypothesis, usually "there is no meaningful difference between A and B," against an alternative hypothesis, such as "the new onboarding email increases activation." The point is not to sound academic. The point is to pre-commit what evidence would change your mind before the numbers arrive. The best workplace version has four parts: the user segment, the intervention, the primary metric, and the expected direction or minimum effect. Sample size enters…
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