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README
A/B Test Setup (Global)
Run experiments that produce decisions, not noise. Most "A/B tests" in marketing are underpowered, peeked-at, and badly hypothesized โ meaning the team learns nothing and ships the louder variant.
For Newbies
A valid A/B test answers one question: "Did this change cause a real improvement, or am I seeing noise?"
To answer it credibly you need four things:
- A specific hypothesis with a numeric prediction
- One variable changed (everything else identical)
- Enough sample to detect the effect you care about
- Statistical significance before you call a winner (typically p < 0.05)
If any one of these is missing, you don't have an A/B test โ you have a coin flip with extra steps.
This is the opening of the README. Read the full README on GitHub.