A/B Test Sample Size Calculator
Free tool · by Daniel HaketBefore you launch a test, find out how much traffic it actually needs. Enter your baseline conversion rate and the smallest uplift worth detecting, and this returns the visitors required per variant — so you don't call a test too early.
Why sample size matters
The most common A/B testing mistake is stopping too early — a few hundred visitors and an exciting early lead that vanishes with more data. Calculating the sample size up front tells you how long to run the test. Smaller effects and lower baseline rates need far more traffic. Decide your minimum detectable effect honestly: chasing a 1% lift on low traffic can take months.
Frequently asked questions
What is statistical power?
Power (usually set at 80%) is the chance your test detects a real effect if one exists. Higher power needs more visitors but reduces the risk of missing a genuine winner.
What's a minimum detectable effect?
It's the smallest improvement you care about catching, as a relative percentage. Detecting a 20% lift needs far less traffic than detecting a 5% lift — be realistic about what's worth testing.
Why do I need so many visitors?
Conversion data is noisy, so distinguishing a real difference from random chance takes volume — especially for small effects or low baseline rates. The calculator shows the honest number.
How do I actually run the test?
Use an experimentation platform like VWO to build variants, split your traffic and measure results — this calculator just tells you how big the test needs to be.
This tool is free and runs entirely in your browser. The link above is an affiliate link: we may earn a commission if you sign up, at no extra cost to you, and it never changes our honest take.