analyze-results¶
data-analysis
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)