A/B Test Statistical Significance Calculator
Compare visitor and conversion totals for two A/B test variants. Choose your confidence level and test direction to see the significance result and supporting figures.
Enter your A/B test results
- Power
- p value
Why statistical significance matters in A/B testing
A higher conversion rate does not always mean a version performs better. Random variation can produce differences between groups, especially when only a few visitors have converted. Calculating statistical significance helps you assess whether the observed difference provides evidence against the assumption that both versions perform equally.
This helps you make more informed decisions about changes to your website, campaigns, or messaging. A significant result provides evidence of a difference under the test’s assumptions; it does not guarantee that the improvement will continue. A result that is not significant means the test has not established a difference, rather than proving the versions are identical.
Choose your hypothesis, confidence level, and sample size before starting the experiment. Compare independent groups, avoid repeatedly stopping as soon as a result becomes significant, and consider whether the size of the improvement is meaningful for your business.
Calculation details
This calculator uses separate estimated variances and a normal approximation. The displayed p value is the upper-tail probability for B minus A, including in two-sided mode; it is not a conventional two-sided p value. One-sided mode uses a full alpha threshold for increases and a half-alpha threshold for decreases. Two-sided mode uses half-alpha thresholds in both tails. Normal probabilities are rounded to five decimals before comparison.
Small samples and conversion rates near 0% or 100% can make this approximation unreliable. Power is estimated from the observed effect, rather than a prospective sample-size plan.
Statistical significance is not the same as business value
A small conversion-rate difference can matter statistically without being worth the cost of a change. Consider the size of the effect and the quality of the experiment as well as the result. Read the calculation details below the tool: its p-value convention is not a conventional two-sided p value.