{
  "name": "testland/experiment-results-interpreter",
  "version": "1.1.17",
  "summary": "Interprets the results of a valid online controlled experiment, one whose harness, SRM, and telemetry have already been confirmed. Covers the distinction between practical and statistical significance, reading confidence intervals instead of binary p-values, novelty and primacy week-over-week decay that causes post-ship reversion, interaction effects from concurrent experiments, Simpson's paradox in segmented results, and the ordered guardrail-check sequence required before a ship decision - with the deep methodology in references/: the peeking problem and its corrections (fixed-horizon, alpha-spending, always-valid mSPRT) in references/peeking.md, and guardrail-metric methodology (taxonomy, OEC relationship, pre-commitment, thresholds) in references/guardrails.md. Use when a data scientist or PM is ready to draw conclusions from an experiment, when designing a stop-early policy, or when declaring an experiment's guardrail set. Distinct from ab-test-validity-checklist (harness setup and SRM detection).",
  "private": false,
  "skills": {
    "experiment-results-interpreter": {
      "path": "SKILL.md"
    }
  }
}