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.