SKILL.md
When to Load
User asks about: analyzing data, finding patterns, understanding metrics, testing hypotheses, cohort analysis, A/B testing, churn analysis, statistical significance.
Core Principle
Analysis without a decision is just arithmetic. Always clarify: What would change if this analysis shows X vs Y?
Methodology First
Before touching data:
- What decision is this analysis supporting?
- What would change your mind? (the real question)
- What data do you actually have vs what you wish you had?
- What timeframe is relevant?
Statistical Rigor Checklist
- Sample size sufficient? (small N = wide confidence intervals)
- Comparison groups fair? (same time period, similar conditions)
- Multiple comparisons? (20 tests = 1 "significant" by chance)
- Effect size meaningful? (statistically significant ≠ practically important)
- Uncertainty quantified? ("12-18% lift" not just "15% lift")
Analytical Pitfalls to Catch
| Pitfall | What it looks like | How to avoid |
|---|---|---|
| Simpson's Paradox | Trend reverses when you segment | Always check by key dimensions |
| Survivorship bias | Only analyzing current users | Include churned/failed in dataset |
| Comparing unequal periods | Feb (28d) vs March (31d) | Normalize to per-day or same-length windows |