A/B Test Runtime Calculator
How long until your A/B test reaches significance? Enter your monthly traffic, baseline conversion rate, the effect you expect and the number of variations — and get the required sample size and test duration instantly.
Enter your traffic and test parameters above, then click Calculate to see the required runtime and the days-per-uplift chart.
Plan your test before you start it
Know upfront how much traffic and time a reliable result will take.
Required sample size
See how many visitors and conversions you need per variation to detect the effect you care about.
Time to significance
Get the test duration in days for your expected effect — and how it shifts as the uplift gets smaller or larger.
Plan with confidence
Choose 90%, 95% or 99% confidence and add variations to see how each choice changes the timeline.
Understanding A/B test runtime
The calculator first works out the sample size needed to detect your expected effect at the chosen confidence level and 80% statistical power, using a two-proportion sample-size formula. It multiplies that by the number of variations to get the total visitors needed, then divides by your monthly traffic and converts to days: roughly total visitors ÷ monthly visitors × 30.
Small differences are harder to distinguish from random noise, so they need a larger sample to reach significance. Halving the effect you want to detect roughly quadruples the required sample size — which is why realistic effect estimates matter a lot for planning.
The calculator shows both the sample size per variation and the total across all variations. The total is what you divide by your traffic to get the runtime, so more variations or lower traffic both push the duration up.
You pick the confidence level (90%, 95% or 99%, two-sided). Statistical power is fixed at 80% — the common default, meaning an 80% chance of detecting a real effect of the size you specified. Higher confidence means a larger sample and a longer test.
Yes — the total sample size scales with the number of variations (including the control). Note that it does not apply a multiple-comparison correction, so with several variations treat the runtime as a lower bound and plan a little extra to stay statistically safe.
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