Experiment Results Analysis for Papers

inno-experiment-analysis

Canonical Summary

This skill should be used when the user asks to "analyze experimental results", "generate results section", "statistical analysis of experiments", "compare model performance", "create results visualization", or mentions connecting experime...

Trigger Rules

Use this skill when the user request matches its research workflow scope. Prefer the bundled resources instead of recreating templates or reference material. Keep outputs traceable to project files, citations, scripts, or upstream evidence.

Resource Use Rules

• Read from references/ only when the current task needs the extra detail.

Execution Contract

• Resolve every relative path from this skill directory first.
• Prefer inspection before mutation when invoking bundled scripts.
• If a required runtime, CLI, credential, or API is unavailable, explain the blocker and continue with the best manual fallback instead of silently skipping the step.
• Do not write generated artifacts back into the skill directory; save them inside the active project workspace.

Upstream Instructions

Results Analysis for ML/AI Research

A systematic experimental results analysis workflow connecting experimental data to paper writing.

Core Features

This skill provides three core capabilities:

1. Experimental Data Analysis - Read and analyze experimental data in various formats
2. Statistical Validation - Perform statistical significance tests and performance comparisons
3. Paper Content Generation - Generate text and visualizations for the Results section

When to Use

Use this skill when you need to:

• Analyze experimental results (CSV, JSON, TensorBoard logs)
• Generate the Results section of a paper
• Compare performance across multiple models
• Perform statistical significance tests
• Create publication-quality visualizations
• Validate the reliability of experimental results

Workflow

Standard Analysis Pipeline

Data Loading → Data Validation → Statistical Analysis → Visualization → Writing → Quality Check

Step 1: Data Loading and Validation

Supported Data Formats:

• CSV files - Tabular data
• JSON files - Structured results
• TensorBoard logs - Training curves
• Python pickle - Complex objects

Data Validation Checks:

• Completeness check - Missing values, outliers
• Consistency check - Data format, units
• Reproducibility check - Random seeds, version info

Select appropriate tools for data loading and preliminary validation based on data format.

Step 2: Statistical Analysis

Basic Statistics:

• Mean
• Standard Deviation
• Standard Error
• Confidence Interval

Significance Tests:

• t-test - Two-group comparison
• ANOVA - Multi-group comparison
• Wilcoxon test - Non-parametric test
• Bonferroni correction - Multiple comparison correction

Select appropriate statistical tests based on data characteristics.

Key Principles:

• Report complete statistical information (mean ± std/SE)
• Specify the test method and significance level used
• Report p-values and effect sizes
• Consider multiple comparison issues

See references/statistical-methods.md for the complete statistical methods guide.

Step 3: Model Performance Comparison

Comparison Dimensions:

• Accuracy/Performance metrics
• Training time/Inference speed
• Model complexity/Parameter count
• Robustness/Generalization ability

Comparison Methods:

• Baseline comparison - Compare with existing methods
• Ablation study - Validate component contributions
• Cross-dataset validation - Test generalization

Systematically compare performance across different methods, ensuring fair comparison.

Step 4: Visualization

Publication-Quality Visualization Requirements:

• Vector format (PDF/EPS)
• Colorblind-friendly palette
• Clear labels and legends
• Appropriate error bars
• Readable in black-and-white print

Common Chart Types:

• Line chart - Training curves, trend analysis
• Bar chart - Performance comparison
• Box plot - Distribution display
• Heatmap - Correlation analysis
• Scatter plot - Relationship display

Use appropriate visualization tools to generate publication-quality figures.

See references/visualization-best-practices.md for the visualization guide.

Step 5: Writing the Results Section

Results Section Structure:

## Results

### Overview of Main Findings
[1-2 paragraphs summarizing core results]

### Experimental Setup
[Brief description of experimental configuration; details in appendix]

### Performance Comparison
[Comparison with baseline methods, including tables and figures]

### Ablation Study
[Validate contributions of each component]

### Statistical Significance
[Report statistical test results]

### Qualitative Analysis
[Case studies, visualization examples]

Writing Principles:

• Clearly state the hypothesis each experiment validates
• Guide readers to observe key phenomena: "Figure X shows..."
• Report complete statistical information
• Honestly report limitations

See references/results-writing-guide.md for the complete writing guide.

Step 6: Quality Check

Checklist:

• All values include error bars/confidence intervals
• Statistical test methods are specified
• Figures are clear and readable (including black-and-white print)
• Hyperparameter search ranges are reported
• Computational resources are specified (GPU type, time)
• Random seed settings are specified
• Results are reproducible (code/data available)

Common Mistakes and Pitfalls

Statistical Errors

❌ Wrong approach:

• Reporting only the best results (cherry-picking)
• Confusing standard deviation and standard error
• Not reporting statistical significance
• Not correcting for multiple comparisons

✅ Correct approach:

• Report all experimental results
• Clearly specify whether standard deviation or standard error is used
• Perform appropriate statistical tests
• Use Bonferroni or similar correction methods