ab-test-analysis

To calculate statistical significance for a/b testing, you need to evaluate p-values and confidence intervals. This Skill performs those statistical calculations to determine if observed differences in your experiment data are real, validating whether your uplift is meaningful.

Sample ratio mismatch is a potential bias in a/b testing where traffic allocation skews from expected proportions. This Skill validates your test setup by checking for adequate sample size, test duration, and potential biases like sample ratio mismatch to ensure reliable experiment analysis.

Validating test setup for a/b testing involves verifying adequate sample size, checking test duration, and monitoring guardrail metrics. This Skill evaluates these experiment parameters to ensure your product analytics data is robust enough for confident, data-driven decisions.

Yes, this Skill provides actionable recommendations to ship, extend, or stop a product feature. It evaluates your a/b test results and guardrail metrics to help you make confident, data-driven decisions about shipping product changes without negatively impacting key metrics.

Yes, this Skill handles confidence interval calculation for conversion rate experiments. It analyzes a/b test results by computing statistical significance and confidence intervals to help you understand if the observed differences in your experiment data are real.

You should extend your a/b test when results lack statistical significance or sample size validation fails. This Skill provides clear recommendations to ship, extend, or stop based on your experiment analysis, p-values, and guardrail metric monitoring.