Measure: Validate With Data

Operationalizes measurement to validate hypotheses and track progress. Specifies instrumentation, designs experiments rigorously, documents results with statistical rigor, and grades goal performance honestly.

1. Use measure-instrumentation-spec to define event tracking requirements so engineering collects the right data. 2. Run measure-experiment-design to design A/B tests and experiments with variants, success metrics, sample size, and duration for a hypothesis. 3. Specify measure-dashboard-requirements for analytics dashboard specs to track KPIs. 4. After experiments conclude, run measure-experiment-results to document learnings with statistical analysis. 5. At cycle close, use measure-okr-grader to score completed OKR sets per the canonical type enum (committed, aspirational, learning, operational_health, compliance). 6. Run measure-survey-analysis on survey responses to segment personas, validate hypotheses, and surface patterns. Output: rigorous validation artifacts that refuse to fabricate statistical significance or overstate confidence.

experiment-design and measure-okr-grader refuse overconfident projections and statistical fabrication. measure-survey-analysis explicitly labels what NOT to conclude. instrumentation-spec produces engineer-ready event taxonomy. All skills lean into rigor and intellectual honesty over false precision.