Plan your experiment before you run it. This free A/B test sample size calculator tells you how many visitors you need per variant to reliably detect the improvement you care about — so you do not stop too early and chase noise, or run forever.
How to use the sample size calculator
Enter your current (baseline) conversion rate and the minimum relative improvement you want to be able to detect. Pick your confidence level and statistical power, and optionally your daily traffic to estimate the test duration. The calculator returns the visitors needed per variant. This pairs perfectly with our A/B test significance calculator, which you use once the data is in.
Frequently asked questions
Why does sample size matter in A/B testing?
Too small a sample and your test cannot tell a real effect from random noise, leading to false winners. Calculating the required sample size up front keeps your results trustworthy.
What is minimum detectable effect?
It is the smallest improvement you want the test to be able to detect, expressed as a relative percentage of your baseline. Smaller effects require much larger samples to detect reliably.
What power and confidence should I use?
The common defaults are 95% confidence and 80% power. Raising either makes your test more rigorous but requires a larger sample size.