The A/B testing calculator for better launch decisions

Plan sample size, model MDE, analyze conversion and revenue results, and catch Sample Ratio Mismatch before you act.

What this calculator answers

• How many visitors each variant needs before an A/B test is ready.
• Which minimum detectable effects are realistic for your traffic and run time.
• Whether p-values, confidence intervals, power, and expected loss support a launch decision.
• Whether observed traffic split suggests Sample Ratio Mismatch or a broken randomization path.
• How conversion rate, Average Order Value, Revenue Per Visitor, and Products Per Visitor moved.

Core concepts

Sample size depends on baseline performance, MDE, confidence, statistical power, test direction, and the number of variants. Statistical significance checks whether an observed difference is unlikely under the no-change assumption. Power helps prevent false negatives, while confidence helps control false positives.

Revenue Per Visitor is total revenue divided by total visitors. It combines conversion rate and Average Order Value while keeping the visitor as the randomization unit, making it the preferred revenue metric for most e-commerce experiments. AOV is still useful as a guardrail when order size changes matter.

SRM stands for Sample Ratio Mismatch. It checks whether the observed visitor counts for each variant match the planned allocation closely enough. A mismatch can point to randomization bugs, bot traffic, consent filtering, targeting mistakes, or analytics collection issues.