analyze-resultslisted

# Analyze Experiment Results Analyze: $ARGUMENTS ## Workflow ### Step 1: Locate Results Find all relevant JSON/CSV result files: - Check `figures/`, `results/`, or project-specific output directories - Parse JSON results into structured data ### Step 2: Build Comparison Table Organize results by: - **Independent variables**: model type, hyperparameters, data config - **Dependent variables**: primary metric (e.g., perplexity, accuracy, loss), secondary metrics - **Delta vs baseline**: always compute relative improvement ### Step 3: Statistical Analysis - If multiple seeds: report mean +/- std, check reproducibility - If sweeping a parameter: identify trends (monotonic, U-shaped, plateau) - Flag outliers or suspicious results ### Step 4: Generate Insights For each finding, structure as: 1. **Observation**: what the data shows (with numbers) 2. **Interpretation**: why this might be happening 3. **Implication**: what this means for the research question 4. **Next step**: what experiment would test the interpretation ### Step 5: Update Documentation If findings are significant: - Propose updates to project notes or experiment reports - Draft a concise finding statement (1-2 sentences) ## Output Format Always include: 1. Raw data table 2. Key findings (numbered, concise) 3. Suggested next experiments (if any) ## 统计纪律（本整合包硬规则） 任何"A 优于 B"的结论必须遵守 `skills/statistical-testing/SKILL.md` 的小样本 规则：n=3 种子只报 mean ± std / 极差，不报 p 值；≥5 种子的配对比较才做显著性 检验（默认 Wilcoxon signed-rank 或 Welch）；