A/B Test Significance Calculator
Compare A/B conversion rates with relative lift, a two-proportion z-test, p-value and confidence interval.
Compare two conversion rates with a two-proportion z-test
Enter visitors and conversions for variants A and B to estimate conversion rates, relative lift, a two-sided p-value and a confidence interval for the absolute rate difference.
What this A/B test significance calculator measures
This calculator compares two independent conversion proportions using a pooled two-proportion z-test. It is useful for experiments such as landing-page, checkout, email or product-flow tests when each visitor is assigned to one variant and the outcome is binary, such as converted or did not convert.
How the calculation works
For each variant, conversion rate equals conversions divided by visitors. Under the null hypothesis that both variants have the same true rate, the test pools the two samples to estimate a common conversion probability. The observed rate difference is divided by its pooled standard error to obtain a z statistic. The calculator reports a two-sided p-value because either variant could outperform the other.
How to interpret the result
A p-value below the selected significance level means the observed difference would be relatively unusual under the equal-rate null model. It does not prove that B is better, estimate the probability that a hypothesis is true, or tell you whether the lift is economically important. Review the absolute difference, confidence interval, sample quality and experiment design together.
When not to rely on the z-test
The normal approximation can be weak with small expected counts. Repeatedly checking a test and stopping as soon as it becomes significant can also inflate false positives. Sequential tests, multiple variants, repeated users and clustered observations may require a different statistical design.