A/B Test Significance Calculator

Statistical significance tells you whether the difference between two variants (A and B) is real or just due to random chance. A result is typically considered significant at a 95% confidence level, meaning there is only a 5% probability the observed difference happened by chance. This significance calculator uses a z-test for two proportions to determine the confidence level of your A/B test results.

The sample size depends on your baseline conversion rate, the minimum detectable effect (how small a difference you want to detect), and your desired confidence level. As a rule of thumb, most PPC A/B tests need at least 1,000 visitors per variant and 25+ conversions per variant to produce reliable results. Use this ab test sample size calculator guideline: the smaller the difference you want to detect, the more traffic you need.

For most PPC split tests, 95% confidence is the standard threshold. This means you accept a 5% chance of a false positive. For high-stakes decisions (large budget changes, landing page overhauls), consider using 99%. For quick iterative tests with lower risk, 90% can be acceptable. This split test calculator shows results at all three levels so you can decide based on your context.

Yes. Enter the number of impressions (or clicks to the landing page) as visitors, and the number of conversions (clicks, leads, or purchases) for each ad variant. This ab test significance calculator works for any two-variant comparison: ad headlines, landing pages, bidding strategies, or audience segments.