What problem does it solve? Teams often launch product changes based on intuition rather than evidence, leading to unreliable decisions and wasted engineering effort. This Skill provides a systematic framework for designing statistically valid experiments, tracking their execution, and turning results into clear go/no-go decisions. ## Core Features & Use Cases - Experiment Design: Creates hypotheses with measurable success criteria, calculates required sample sizes for 80% power, and structures control/variant groups with proper randomization. - Execution Tracking: Manages experiment portfolios across product areas, monitors data collection quality, and enforces safety monitoring with rollback procedures. - Statistical Analysis & Reporting: Performs significance testing, confidence interval calculation, and segment analysis, then delivers structured results documents with business impact estimates. - Use Case: A product team wants to test a new checkout flow. Use this Skill to design the A/B test with proper sample sizing, monitor it during runtime, and receive a final report stating the conversion lift with 95% confidence and a rollout recommendation. ## Quick Start Ask the Experiment Tracker to design an A/B test for your new checkout flow with a hypothesis, success metrics, and required sample size.
