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A/B test significance calculator
Enter the visitors and conversions for each version to see the conversion rates, the uplift and whether the difference is statistically significant, plus the sample size you need.
- Free, no sign-up
- Results in seconds
- Built by the JuvioX team
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Quick answer
How do you know if an A/B test result is significant?
Compare the conversion rates of both versions and check how likely the difference is to be real rather than chance. Most teams use 95% confidence: at that level, there is less than a 5% chance of seeing a difference this large if both versions actually performed the same. This calculator does the math from your visitors and conversions.
How it works
Three steps, no sign-up
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01
Enter both versions
Visitors and conversions for version A (control) and version B.
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02
Choose a confidence level
95% is the usual standard. Use 90% for quick directional tests and 99% for high-stakes changes.
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03
Read the verdict
See the uplift, the p-value and how many visitors each version needs to reach a reliable answer.
FAQ
Questions about this tool
Need more than a tool? See how our Conversion Rate Optimization service works.
Conversion Rate OptimizationWhat does statistically significant mean?
That the difference between the versions is unlikely to be random. At 95% confidence, a result this large would appear by chance less than 5% of the time if the versions really performed the same.
Can I stop a test as soon as it is significant?
Better not. Checking often and stopping at the first significant result inflates false winners. Decide the sample size up front and run at least one or two full weeks to cover weekday and weekend behaviour.
Which test does this calculator use?
A two-proportion z-test on the conversion rates, the standard method for comparing two conversion rates, with a sample size estimate at 80% statistical power.
How long should an A/B test run?
At least one to two full weeks, so every day of the week is included, and until each version has enough conversions to reach significance. Decide the sample size before you start.
What should I A/B test first?
Start with what most visitors see and what affects their decision: the headline, the offer, the main call to action and the form. Small changes to colors or fonts rarely make a measurable difference.
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