Data Analysis

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:

1. What decision is this analysis supporting?
2. What would change your mind? (the real question)
3. What data do you actually have vs what you wish you had?
4. 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 | | 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

1. Lead with the insight, not the methodology
2. Quantify uncertainty — ranges, not point estimates
3. State limitations — what this analysis can't tell you
4. 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