📖 技能介紹

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
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.

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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。