ab-test-analysis

To determine statistical significance in A/B testing, calculate p-values and confidence intervals to verify if observed metric differences are reliable. This analysis evaluates whether the conversion rate lift is genuine or due to random chance before making product decisions.

Evaluating guardrail metrics during experiment analysis involves checking secondary metrics for unintended negative consequences. This process ensures a new feature rollout improves the target conversion rate without degrading existing product metrics.

Validating sample size and power for an A/B test confirms whether the experiment collected enough data to reliably detect the expected effect size. This power analysis prevents false positives and ensures accurate hypothesis validation.

You should ship, extend, or stop an A/B test based on a decision framework evaluating statistical significance and observed lift. Ship validated hypotheses, extend tests lacking sample size, and stop experiments showing negative guardrail metric impacts.

You can analyze split test data from a CSV file containing experiment results to guide product decisions. The analysis processes the attached data to calculate statistical significance and validate the hypothesis for feature rollouts.