A/B Test Significance Calculator

Statistical significance measures how confident you can be that the difference between your control and variant is real, not due to random chance. A result is typically considered statistically significant at 95% confidence, meaning there is only a 5% probability the observed difference happened by chance. Higher confidence levels (99%) provide stronger certainty but require larger sample sizes.

The required sample size depends on your baseline conversion rate and the minimum detectable effect you want to measure. Generally, you need at least 1,000 visitors per variation for reliable results. For small conversion rate differences (less than 1%), you may need 10,000 or more visitors per variation. This calculator estimates the sample size needed based on your inputs.

The industry standard is 95% confidence, which means you accept a 5% chance of a false positive. For high-stakes decisions like pricing changes or major redesigns, use 99% confidence. For low-risk experiments like button color changes, 90% confidence may be acceptable. The higher the confidence level, the more data you need to reach significance.

Stop a test when it reaches statistical significance at your chosen confidence level AND you have collected enough data for at least one full business cycle (typically 1-2 weeks). Never stop a test early just because results look promising as this leads to false positives. Also avoid running tests too long as external factors can skew results. Pre-determine your sample size and duration before starting.