| name | data-analysis |
| description | Generate statistical analysis code with 4-round review. Select appropriate statistical tests, interpret results, and produce analysis reports with p-values, effect sizes, and confidence intervals. Use when analyzing experimental data for a paper. |
| argument-hint | ["data-source"] |
Data Analysis
Generate rigorous statistical analysis code with multi-round review.
Input
$0 — Data source (CSV, JSON, pickle, or experiment logs)$1 — Research goal or hypothesis to test
References
- 4-round code review prompts:
~/.claude/skills/data-analysis/references/review-prompts.md
Scripts
Statistical summary and comparison
python ~/.claude/skills/data-analysis/scripts/stat_summary.py --input results.csv --compare method --metric accuracy --output summary.json
python ~/.claude/skills/data-analysis/scripts/stat_summary.py --input results.csv --describe
Detects data types, recommends tests, runs comparisons, outputs effect sizes and significance stars. Requires numpy, scipy.
Format p-values
python ~/.claude/skills/data-analysis/scripts/format_pvalue.py --values "0.001 0.05 0.23" --format stars
python ~/.claude/skills/data-analysis/scripts/format_pvalue.py --csv results.csv --column pvalue --format latex
Formats p-values with stars, LaTeX notation, or plain text. Stdlib-only.
Workflow
Step 1: Generate Analysis Code
Structure the code with these sections:
# IMPORT — pandas, numpy, scipy, statsmodels, sklearn# LOAD DATA — Load from original data files# DATASET PREPARATIONS — Missing values, units, exclusion criteria# DESCRIPTIVE STATISTICS — Summary tables if needed# PREPROCESSING — Dummy variables, normalization# ANALYSIS — Statistical tests per hypothesis# SAVE ADDITIONAL RESULTS — Extra results to pickle
Step 2: 4-Round Code Review
- Round 1 — Code Flaws: Mathematical/statistical errors, wrong calculations, trivial tests
- Round 2 — Data Handling: Missing values, units, preprocessing, test choice
- Round 3 — Per-Table: Sensible values, measures of uncertainty, missing data
- Round 4 — Cross-Table: Completeness, consistency, missing variables
Step 3: Produce Results
- Every nominal value must have uncertainty (CI, STD, or p-value)
- Statistical tests must be appropriate for the data type
- Results must match actual data — never hallucinate
Allowed Packages
pandas, numpy, scipy, statsmodels, sklearn, pickle
Statistical Test Selection
| Data Type | Test |
|---|
| Two groups, normal | Independent t-test |
| Two groups, non-normal | Mann-Whitney U |
| Paired samples | Paired t-test / Wilcoxon |
| Multiple groups | ANOVA / Kruskal-Wallis |
| Categorical | Chi-square / Fisher's exact |
| Correlation | Pearson / Spearman |
| Regression | OLS / Logistic / Mixed effects |
Rules
- Always report p-values for statistical tests
- Account for relevant confounding variables
- Use inherent package functionality (e.g.,
formula = "y ~ a * b" for interactions)
- Do not manually implement available statistical functions
- Access dataframes using string-based column names, not integer indices
Related Skills
