Result
Visitors needed per variant
—
Your inputs are stored in this browser, so everything is still here next time. Nothing is sent to a server.
This calculation is for informational purposes only and does not replace advice from a qualified professional. Formulas and rates may not fit your exact situation — double-check the figures before making decisions.
How it is calculated
The calculation uses standard A/B test planning assumptions: 95% two-sided significance and 80% statistical power — the most common pairing, though not the only possible one. Different reliability requirements would call for a different sample size.
The minimum detectable effect is relative, not absolute: a value of 10% at a 5% baseline conversion means the test needs to reliably tell 5% apart from 5.5%, not from 15%. The smaller the effect you need to detect, the more visitors you need — and the relationship isn't linear but much steeper: detecting an effect half as large typically needs roughly four times more data.
This calculation happens before a test launches, not after — it answers "how much data do I need to collect before the result can even be trusted." Checking whether an already-run test's result turned out statistically significant is the job of the A/B test significance calculator.