A/B Test Sample Size & Power
Calculate required sample size for A/B tests with statistical power and MDE analysis. Enter values for instant results with step-by-step formulas.
Worked Examples
Example 1: E-commerce Checkout
Problem:Baseline 2%, MDE 10% (Relative), Power 80%, Sig 5%
Solution:2% -> 2.2% (0.2 percentage-point lift). Using the calculator's two-variant frequentist sample-size formula, you need about 80,706 visitors per variation to reliably detect that change.
Result:161,412 Total Visitors
Example 2: High Traffic Landing Page
Problem:Baseline 15%, MDE 5% (Relative), Power 90%, Sig 5%
Solution:15% -> 15.75% (0.75 percentage-point lift). At 90% power and 95% confidence, the calculator estimates roughly 48,622 visitors per variation.
Result:97,244 Total Visitors
Frequently Asked Questions
Why does 80% Power matter?
Power (1 - β) is the probability of correctly finding a winner if one actually exists. 80% Power means you have a 20% chance of missing a real winner (Type II error). 80% is the industry standard balance between risk and traffic needs.
Can I stop the test early?
No! Stopping as soon as you see significance ('peeking') dramatically increases your False Positive rate. You must commit to the sample size beforehand.
How long should I run the test?
Divide the Total Sample Size by your daily traffic. Ideally, run for full business cycles (e.g., 2 full weeks) to account for day-of-week variances.