ab-testing-statistics

Selecting the right statistical test for A/B testing depends on your metric type: use a two-proportion z-test for binary metrics and Welch's t-test for continuous metrics to ensure valid conclusions.

Power analysis in A/B testing is a technique used during experiment planning to determine the required sample size, ensuring your test has enough statistical power to detect a meaningful effect size before you begin data collection.

To handle multiple comparisons in A/B testing, apply multiple comparison corrections to control error rates, while calculating effect sizes and relative lift provides a standardized measure of the actual magnitude of the observed difference.

The best way to interpret p-values in A/B testing is to follow a reproducible workflow that guides data preparation, test selection, significance interpretation, and reporting, ensuring your conclusions are statistically sound.

You need a dataset large enough to meet the sample size requirements determined by your power analysis; running A/B tests without calculating required sample sizes risks false negatives and invalid conclusions.