Sample size calculator
Estimate the sample size required for an A/B test based on the minimum effect of interest and the required confidence threshold.Try it now
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A/B test sample size calculator
- Works with both binomial and non-binomial metrics
- Sample size estimation for tests with multiple variants (A/B/N tests)
- Supports non-inferiority A/B test designs
- Calculates sample sizes required for analysis of statistics for percent change (lift)
- Excellent documentation and presentation of calculation results
Sample size calculations done right
Support for non-binomial metrics
On top of binomial metrics such as conversion rates, the calculator supports sample size calculation for tests with a primary KPI which is a non-binomial (continuous) metrics such as average revenue per user, average sessions per user, average session duration, average order value, etc. out of the box.
An easy-to-use interface allows you to upload a file with your non-binomial data and all relevant statistics are calculated from it.
Properly plan A/B/N tests
Tests with more than one variant versus a control need to be analyzed with special methods that account for the multiple comparisons problem that otherwise arises. The appropriate p-value and confidence interval correction is the Dunnett's correction which means that a sample size calculation should take these corrections into account.
This tool does just that so your A/B/N tests have the right sample size requirement and therefore duration to hit your desired power level.
Plan non-inferiority tests
If you have planned a test with a non-inferiority hypothesis it needs to be analyzed as such. This significance calculator has native support for non-inferiority tests.
Simply enter the non-inferiority margin used when planning the test and the tool will take care of the rest.
Sample size for lift estimation
Calculating p-values and confidence intervals for percent change (% lift) requires that this is taken into account when considering the required sample size. This tool uses a proprietary formula to make these calculations.
This tool is documented in detail, with information about the statistical assumptions being displayed below each computation. Inline help information assists in using it correctly and makes the statistical jargon accessible by presenting with terms familiar to professionals engaged in online A/B testing.
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