Growth Engineering Playbook
Why your A/B test is inconclusive
Paste your test numbers and get an honest verdict — statistical significance, power, the effect you can actually detect, and how long you'd really need to run it. No backend, nothing leaves your browser.
Numbers reflect the fictional Northstar Outfitters store used across
this playbook's shared-data.
Enter your numbers
Results update as you type. This tool errs toward honesty: an underpowered test is reported as "not enough evidence", not a win.
- p-value (two-sided)
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- Observed absolute lift
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- 95% CI for the difference
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- Statistical power (observed)
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- Required sample / variant
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- Time to significance
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Peeking simulator
Why does "just checking" hurt? This runs many A/A experiments where both variants have the same true rate — so every "win" is a false positive. Testing once at the end holds the false positive rate near your threshold. Peeking after every checkpoint and stopping at the first significant result inflates it.