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文档润色流和知识库构建流/claude-scholar/skills/results-analysis/references/common-pitfalls.md

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+# Common Pitfalls in Experimental Analysis
+
+## Statistical pitfalls
+- Reporting only the best run
+- Mixing seed-level and subject-level units
+- Running many contrasts without correction
+- Reporting significance without effect size
+- Using parametric tests after failed assumptions without explanation
+
+## Visualization pitfalls
+- No real figure despite readable data
+- Plot without uncertainty information
+- Overcrowded multi-panel figure with no message hierarchy
+- Caption missing n / error-bar meaning
+- Figure not referenced or interpreted in text
+
+## Reasoning pitfalls
+- Confusing correlation with mechanism
+- Treating trend as conclusion
+- Ignoring negative results
+- Hiding instability behind a mean value
+- Turning raw logs into durable conclusions too early
+
+## Reporting pitfalls
+- Writing paper prose before evidence is stabilized
+- Mixing analysis artifact with final narrative artifact
+- Not separating blocker from conclusion
+- Forgetting to state what decision the analysis changes