A/B Test Statistical Significance Calculator
Enter visitors and conversions for both versions to see, with a chi-squared test, whether the result is significant, how large the uplift is and which variant won.
A/B Test and Statistical Significance Calculation
This calculator checks whether a change in a campaign or on a website (button color, headline, image, etc.) actually affected the result. It runs a statistical significance test to tell you whether the gap in conversion rate between two variations comes from the change or from chance.
How Do You Interpret A/B Test Results?
Two numbers matter when you read the result: Conversion Rate and Confidence Level. If one version does better than the other at 95% confidence or higher, the result counts as 'significant' and the change can be made permanent.
P-Value and Significance Level
The p-value is the probability of seeing a difference at least as large as the one you observed if there were no real difference between A and B. It is not the probability that the difference is due to chance. The usual requirement is a p-value below 0.05. The lower it is, the harder it is to explain the result by chance alone.
Why Does Sample Size Matter?
An A/B test needs to reach enough users (traffic) to give an accurate result. Tests run on too little data can be misleading. Based on the sample size you enter, the calculator shows how far the result stands out from 'noise'.
Why Does Statistical Significance Matter in A/B Testing?
Statistical significance tells you whether the conversion difference between two versions is real or just chance. Decisions made without it can rest on nothing more than random variation. The usual threshold is a 95% confidence level, reached with an adequate sample size.
How Do You Set Sample Size and Test Duration?
Too small a sample is the most common source of error in A/B tests. The sample size and duration you need depend on the current conversion rate, the minimum detectable effect and the confidence level you choose. Most digital marketing platforms recommend at least one thousand visitors and two weeks of testing. On low-traffic pages the test should run longer.
Reading A/B Test Results Correctly
A significant result is not guaranteed to be correct. Testing several variants at once raises the error risk (the multiple comparison problem), and short tests can be skewed by unusual traffic swings. Run the test long enough to cover full business cycles, and send both variants traffic from similar sources.