ab-test-analysis

To analyze A/B test results for statistical significance, the Skill evaluates whether observed differences in conversion rates are due to the product change or random chance, validating sample size and test duration.

Evaluating guardrail metrics during experiment analysis involves assessing the impact on secondary metrics to identify unintended negative consequences, ensuring that product iterations do not harm overall user engagement.

To know if your split test has enough sample size and duration, the Skill validates the collected data against required thresholds, ensuring the experiment ran long enough to yield data-driven insights.

Yes, you can get actionable recommendations to ship, extend, stop, or investigate experiments based on your A/B test data, providing clear reasoning for product iterations and decision making.

You should not use an automated tool for A/B testing analysis when tests lack sufficient sample size or duration, as validating these constraints is required to accurately determine statistical significance.