A/B Test Calculator
Size your test before you launch, and check significance once results come in. Two calculators, no signup, no spreadsheet.
Assumes 80% statistical power.
Stop calculating. Start testing.
Drop in your site and Dalton finds the experiment, sizes it, builds it, and ships the winner for you.
A/B test sample size calculator
Running a test without enough traffic is how teams ship false winners. The sample size calculator tells you how many visitors each variant needs before the result can be trusted. It uses your baseline conversion rate, the minimum detectable effect you want to catch, and your chosen significance and power. Lower conversion rates and smaller effects need more visitors, which is why low-traffic sites struggle with traditional A/B testing. For more detail, see the dedicated sample size calculator.
Statistical significance calculator
Once a test has run, the significance calculator checks whether the difference between your control and variant is real or just noise. Enter visitors and conversions for both groups and it runs a two-proportion z-test, returning the confidence level, the p-value, and a clear verdict at your threshold. Ninety-five percent confidence is the standard for most conversion tests. See the dedicated statistical significance calculator.
Frequently asked questions
How do I calculate the sample size for an A/B test?
Enter your baseline conversion rate and the minimum detectable effect you care about, then pick your significance and power. The calculator returns the number of visitors needed per variant using the standard two-proportion formula.
What is a good sample size for an A/B test?
There is no fixed number. It depends on your baseline conversion rate and the size of the effect you want to detect. Smaller effects and lower conversion rates need more visitors. Use the calculator above to get the exact figure for your case.
How do I know if my A/B test result is statistically significant?
Switch to the Significance tab and enter visitors and conversions for both the control and the variant. The calculator runs a two-proportion z-test and tells you the confidence level, the p-value, and whether the result clears your threshold.
What does statistical significance mean?
It is the confidence that the difference between your variant and control is real rather than random chance. A 95 percent threshold means there is a 5 percent chance you would see a result this extreme if there were truly no difference.
What significance level should I use?
95 percent is the standard for most conversion tests. Use 99 percent when the change is risky or expensive to get wrong, and 90 percent only for low-stakes, early-read experiments.