experiment-framework

Sample size calculation for an A/B test requires baseline rates and a desired minimum detectable effect (MDE) to ensure statistical rigor. This framework uses those inputs to generate the necessary experiment specifications for validating product changes.

Guardrail metrics in product experimentation are critical indicators monitored to ensure a new feature does not cause unintended harm, such as increased error rates or page load time degradation. They are defined alongside primary and secondary metrics during test design.

Designing an A/B test to validate feature impact involves defining clear hypotheses, structuring primary and guardrail metrics, and calculating proper sample sizes. This framework generates experiment specifications from inputs like PRD success metrics and feature flag specs.

Yes, you can analyze existing A/B test results by providing analytics configurations and outcome data. The framework applies statistical analysis to evaluate the impact, ensuring product decisions are driven by validated data rather than noise.

Setting up a statistically rigorous experiment requires inputs like PRD success metrics, feature flag specs, and analytics configurations. These elements allow the framework to define hypotheses, calculate sample sizes, and structure the results analysis.

Statistical analysis for A/B testing is necessary when you need to prevent decisions driven by data noise and ensure feature impact is accurately measured. Use it to validate conversion rate changes and confirm that critical guardrail metrics do not degrade.