ab-test-analysis

Analyze A/B test results by calculating p-values and confidence intervals to determine if observed differences between variants are statistically significant. This validates whether the experiment outcome is reliable rather than due to random chance.

Validate A/B test sample size and duration by checking if the experiment gathered adequate data volume and ran long enough to yield reliable results. This ensures the statistical significance calculations are trustworthy for decision making.

Interpret split test data by evaluating statistical significance, practical impact, and guardrail metrics. The analysis provides clear decision recommendations—Ship, Extend, Stop, or Investigate—based on the experiment outcomes.

Yes, you can monitor guardrail metrics during experiment analysis to assess the impact on secondary metrics. This prevents unintended negative consequences when evaluating product metrics and deciding whether to ship a variant.

No, Python script generation for raw data is optional. The core experiment analysis calculates statistical significance, sample size, and confidence intervals without requiring custom Python scripts.

An A/B test result may show no statistical significance due to inadequate sample size, insufficient test duration, or negligible differences between variants. Extending the experiment or investigating guardrail metrics can help clarify the outcome.