A/B Test P-Value Calculator
Find out whether the difference between your control and variant is statistically significant. Enter the visitors and conversions for each group to compute the p-value, z-score and observed lift in real time.
Control (A)
Variant (B)
How the p-value is calculated
This calculator runs a two-proportion z-test, the standard significance test for A/B experiments with binary outcomes such as sign-ups, purchases or clicks. It first computes each group's conversion rate, p_A = c_A / n_A and p_B = c_B / n_B. Under the null hypothesis the two rates are equal, so the variances are estimated from a pooled conversion rate p = (c_A + c_B) / (n_A + n_B).
The standard error of the difference is SE = sqrt(p·(1−p)·(1/n_A + 1/n_B)), and the test statistic is the z-score z = (p_B − p_A) / SE. The z-score is converted to a p-value using the standard normal cumulative distribution, approximated here with the Abramowitz & Stegun error-function formula (accurate to about 7 decimal places). A two-tailed test reports 2·(1 − Φ(|z|)); a one-tailed test reports 1 − Φ(z).
The p-value is the probability of seeing a difference at least this large purely by chance if the variants truly performed the same. When the p-value falls below your chosen alpha (0.05 means 95% confidence) the result is declared statistically significant and you can reject the null hypothesis. A non-significant result does not prove the variants are equal — it usually means you need more samples. Significance ignores business impact, so always read it alongside the observed lift and absolute volume before shipping a winner.