# Results Analysis

Use when analyzing experimental results, validating comparisons, generating paper-ready...

- Repository: https://github.com/Boom5426/Nature-Paper-Skills (646 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-10-13
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add Boom5426/Nature-Paper-Skills --skill results-analysis -g -a claude-code -y`
Codex: `npx skills add Boom5426/Nature-Paper-Skills --skill results-analysis -g -a codex -y`
Cursor: `npx skills add Boom5426/Nature-Paper-Skills --skill results-analysis -g -a cursor -y`
Gemini CLI: `npx skills add Boom5426/Nature-Paper-Skills --skill results-analysis -g -a gemini-cli -y`

## Description

Use when analyzing experimental results, validating comparisons, generating paper-ready results text, or turning model-evaluation outputs into figures, tables, and defensible claims.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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.

The rest: https://github.com/Boom5426/Nature-Paper-Skills/blob/HEAD/skills/research/results-analysis/SKILL.md