exp-driven-dev

To design an A/B test, follow the HITS framework: define a Hypothesis, plan the Implementation, run the Test, and decide to Ship or Stop. This structured approach ensures data-backed product iteration and risk mitigation.

Track primary metrics to measure success, such as a 5% increase in purchase completion rate, and guardrail metrics to prevent regressions, ensuring cart abandonment doesn't increase during your A/B testing.

Feature flagging supports data-driven development by allowing controlled rollout and testing of new features. It enables teams to toggle functionality on or off, facilitating safe experimentation and mitigating the risk of negative outcomes.

The best way to calculate statistical significance is by applying established A/B testing methodologies from industry leaders to your product analytics. This validates whether observed metric changes are meaningful rather than random variance.

Yes, you can use experimentation-driven development for a user onboarding flow by generating an experiment spec. This allows you to test variations, measure impact on key metrics, and iterate based on statistical evidence.