A/B Test Statistical Significance Calculator
Calculate the statistical significance, uplift and winning variant of your A/B test using the chi-squared test.
Are Your A/B Test Results Truly Significant?
If your variant generated 15% more conversions than the control, is that difference real or just chance? The A/B test statistical significance calculator uses the chi-squared test to answer this question with a numerical confidence level. No statistics knowledge required — just enter the visitor and conversion counts for your control and variant groups.
How Does It Work? The Chi-Squared Test
The calculator evaluates the conversion rate difference between two groups using the chi-squared (χ²) test. This test measures how far the observed conversion distribution deviates from an independent distribution. The p-value derived from the chi-squared statistic with 1 degree of freedom gives the probability that the observed difference is due to chance. If the p-value is below 0.05 (significance above 95%), the result is considered statistically significant.
A/B Test Metrics: What Do They Mean?
Conversion Rate (CR)
Conversions / Visitors × 100. Calculated separately for each group. Even small absolute differences (e.g. 2% → 2.4%) can carry great commercial value.
Uplift
How much better (or worse) the variant performs compared to the control: (VariantCR − ControlCR) / ControlCR × 100.
Statistical Significance
How confident you are that the observed difference is not random. The 95% standard means that in 100 tests, 5 may produce a false positive.
Common A/B Testing Mistakes
- Stopping early: Closing the test as soon as results look 'significant' leads to false positives.
- Testing multiple changes at once: You can't tell which change was effective.
- Insufficient sample: Low traffic makes it hard to separate noise from signal.
- Ignoring seasonal effects: Tests that don't cover weekly cycles can be misleading.