A/B test calculator
Enter successes and total users for variants A and B. The result compares two independent proportions.
Variant conversion rates
Bars show the observed success share for variants A and B.
Variant data
| Variant | Successes | Sample size | Conversion |
|---|---|---|---|
| Fill in the fields above — the result is computed on this page. | |||
How to use this page
- Enter successes and totals for A and B.
- Choose the confidence level specified before analysis.
- Read the difference, interval and p value.
What this page does not do: The normal approximation is intended for independent groups and adequate counts. It does not handle paired data, repeated peeking or multiplicity across metrics.
Further reading
The two-proportion test evaluates the null hypothesis that A and B have the same success probability. The p value measures compatibility with that hypothesis, while the confidence interval gives a range of plausible B − A differences.
Statistical significance does not establish practical value. Also inspect the effect size, cost, experiment duration and the metric defined before collecting data.
Frequently asked questions
Does a smaller p value mean a larger effect?
No. The p value also depends on sample size. Use the absolute difference, uplift and confidence interval for effect size.
Can I stop as soon as p falls below 0.05?
Repeated unplanned peeking raises false-positive risk. Fix a duration or sample size in advance, or use a sequential method.
See also
A/B test planning — sample size, MDE and duration
Plan a two-proportion experiment, estimate detectable effect and traffic duration.
ACF and PACF autocorrelation calculator
Calculate ACF and PACF across time-series lags. Review the chart, reference bounds and coefficient table.
Adjusted R-squared calculator
Convert R² to adjusted R² using the number of observations and predictors in a regression model.