A/B Test Sample Size calculator.
This A/B test sample size calculator tells you the visitors per variant you need to detect a real lift — and how many weeks that will take at your current traffic. Enter your numbers below.
What are you testing?
How to calculate A/B test sample size.
Sample size for an A/B test answers one question: how many visitors does each variant need before a difference in conversion rate is unlikely to be noise? It depends on four inputs — your baseline rate, the size of the lift you want to detect, how confident you want to be, and how often you are willing to miss a real winner.
Baseline conversion rate (p1)
Your current conversion rate. Lower baselines need far more traffic, because the absolute difference you are chasing is smaller.
Minimum detectable effect (MDE)
The smallest relative lift worth caring about. A 10% MDE on a 3% baseline means detecting a move to 3.3%. Smaller MDEs need much larger samples.
Statistical significance
How sure you want to be that a result is real and not chance. 95% (alpha 0.05) is the convention, giving a two-tailed Z of 1.96.
Statistical power
The chance of detecting a real effect when one exists. 80% is standard, giving a Z of 0.84. Higher power needs more visitors.
This calculator uses the standard two-proportion sample size formula. For each variant:
n = (Z_alpha/2 + Z_beta)^2 * [p1(1 - p1) + p2(1 - p2)] / (p2 - p1)^2
- p1 is the baseline conversion rate; p2 is the baseline lifted by your MDE — p2 = p1 × (1 + MDE).
- Z_alpha/2 is 1.645 for 90% significance, 1.96 for 95%, and 2.576 for 99%.
- Z_beta is 0.84 for 80% power and 1.28 for 90% power.
- Estimated duration is the total required sample (2 × n) divided by your weekly visitors, rounded up to whole weeks.
When that duration runs long — past two or three months — the issue usually is not your test design. It is traffic. That is the wedge Optimize Pilot's Flight Path is built around: it checks whether you have the traffic to test before it tells you to test, and routes low-traffic pages to SEO and demand work first.
Know your sample size. Get started.
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