Why most A/B tests mislead
Peeking at results early, stopping the moment you "win," and ignoring sample size are the fastest ways to ship a change that does nothing. Good experimentation is as much discipline as statistics.
Run it properly
- Fix your sample size and duration before you start.
- Pick one primary metric; treat the rest as guardrails.
- Don't stop early just because a result looks good.
- Account for seasonality and novelty effects.
Read the result honestly
A non-significant result is still information. We report confidence intervals, not just a winner, so you know how much to trust the lift before rolling it out.
