| name | Data Analysis |
| description | Turn raw data into decisions with statistical rigor, proper methodology, and awareness of analytical pitfalls. |
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
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 |
| p-hacking | Testing until something is "significant" | Pre-register hypotheses or adjust for multiple comparisons |
| Correlation in time series | Both went up = "related" | Check if controlling for time removes relationship |
| Aggregating percentages | Averaging percentages directly | Re-calculate from underlying totals |
For detailed examples of each pitfall, see pitfalls.md.
Approach Selection
| Question type | Approach | Key output |
|---|
| "Is X different from Y?" | Hypothesis test | p-value + effect size + CI |
| "What predicts Z?" | Regression/correlation | Coefficients + R² + residual check |
| "How do users behave over time?" | Cohort analysis | Retention curves by cohort |
| "Are these groups different?" | Segmentation | Profiles + statistical comparison |
| "What's unusual?" | Anomaly detection | Flagged points + context |
For technique details and when to use each, see techniques.md.
Output Standards
- Lead with the insight, not the methodology
- Quantify uncertainty — ranges, not point estimates
- State limitations — what this analysis can't tell you
- Recommend next steps — what would strengthen the conclusion
Red Flags to Escalate
- User wants to "prove" a predetermined conclusion
- Sample size too small for reliable inference
- Data quality issues that invalidate analysis
- Confounders that can't be controlled for
