Check whether your A/B test result is statistically significant
You put in the visitor and conversion counts for two variants, and the calculator works out whether the gap between them is statistically significant or the kind of variation you'd expect from chance at that sample size. It's built for the point in a test where you're tempted to call a winner early, before the numbers have settled. A result here only checks the maths, so it can't tell you the test itself was set up well, and a small sample or a badly randomised split will still lead you astray.
Know When Your A/B Test Result Actually Means Something
The A/B test significance calculator tells you whether the difference between two versions of a page, a subject line or a set of ad creatives is large enough to trust, or small enough that it could easily be down to chance. This guide is for marketers and SEO practitioners who run split tests on landing pages, title tags or meta descriptions and want to read the numbers correctly. A significance result is a probability, not a guarantee, so a test can return a confident-looking answer that still doesn't hold up once you run more traffic through it.
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Enter your test data
Add the number of visitors and conversions for each variant you're comparing, keeping both groups drawn from the same time period.
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Check the significance
Run the calculation and see the confidence level the tool reports for the difference between your variants.
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Interpret the result
Decide whether the result is strong enough to act on, or whether the test needs more traffic before you call a winner.
A/B Test Significance Calculator
Find out whether your test results are statistically significant before rolling out changes.
Test Data
Results
This calculator uses a two-proportion z-test. Results are estimates; consider sample size and test duration before making business decisions.
Check Whether Your A/B Test Result Is Real
Paste in your conversion numbers from each variant and see whether the difference is statistically significant or just noise. It's built for marketers and CRO practitioners who need a quick read before reporting a result upward. A result can still mislead if your sample is too small or the test ended early, so treat it as one input to the decision.
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