Interpret your A/B test results correctly — significance, confidence intervals, sample ratio mismatch, and ship/iterate/kill decisions — without falling for common statistical traps.
## CONTEXT Most A/B test failures are not failures of ideas but of interpretation: peeking early, calling winners on noise, ignoring sample ratio mismatch, or chasing segment results that are just multiple-comparison artifacts. In 2026, even with mature tools, teams routinely ship "winners" that do not replicate. The…
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