Data Analysis Litiao
📖 技能介紹
name: data-analysis-litiao 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: 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")
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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
🤖 AI 評測
這個 Skill 質量不錯，內容專業且實用。它系統地整理了資料分析的核心方法論，包括如何提出好問題、識別常見分析陷阱、選擇合適的技術手段，並給出了清晰的輸出標準。美中不足的是缺少使用示例和入門指南，方法論內容較為抽象，對新手不太友好；部分技術細節也較為簡略，需要配合其他資料才能真正上手。適合有一定分析經驗的使用者參考使用。
