Check if your split test results are statistically significant.
This A/B Test Significance Calculator is a vital tool for marketers, product managers, and UX designers. When you run a split test (like testing a red button vs. a green button), you can't just look at the raw numbers to decide a winner. Random chance can often make one variation look better than the other purely by accident.
This tool uses statistical mathematics (specifically a Z-test) to tell you if your results are "Significant." Significance means you can be confident that the difference in performance is due to the change you made, not just random luck. Generally, you want a confidence level of 95% or higher before declaring a winner.
Statistical significance is a way of proving that a specific result isn't just a coincidence. In A/B testing, if a result is "95% significant," it means there is only a 5% chance that the difference between your variations happened by random chance.
The P-value is a number between 0 and 1 that measures the evidence against the null hypothesis. A lower P-value (typically less than 0.05) means it is highly unlikely the results are random, so the test is significant.
You should run a test until you reach statistical significance (usually 95%) AND you have a sufficient sample size. Stopping a test too early, even if it looks significant, can lead to "false positives." It is usually recommended to run a test for at least one full business cycle (e.g., 1 or 2 weeks) to account for weekday vs. weekend behavior.
Conversion Rate is the percentage of visitors who completed the desired action. It is calculated as (Conversions / Visitors) * 100.
Yes. This calculator runs 100% in your web browser using JavaScript. None of your traffic or conversion data is sent to our servers.