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Data Analysis · Research / AGENT SKILL
data-analysis
0
Generate reviewed statistical analysis code and reports for experimental research data.
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.
Understand the trade-offs.
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The full skill.
Original instructions from the publisher’s SKILL.md
# 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 ```bash 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 ```bash 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: 1. `# IMPORT` — pandas, numpy, scipy, statsmodels, sklearn 2. `# LOAD DATA` — Load from original data files 3. `# DATASET PREPARATIONS` — Missing values, units, exclusion criteria 4. `# DESCRIPTIVE STATISTICS` — Summary tables if needed 5. `# PREPROCESSING` — Dummy variables, normalization 6. `# ANALYSIS` — Statistical tests per hypothesis 7. `# SAVE ADDITIONAL RESULTS` — Extra results to pickle ### Step 2: 4-Round Code Review 1. **Round 1 — Code Flaws**: Mathematical/statistical errors, wrong calculations, trivial tests 2. **Round 2 — Data Handling**: Missing values, units, preprocessing, test choice 3. **Round 3 — Per-Table**: Sensible values, measures of uncertainty, missing data 4. **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 - Upstream: [experiment-code](../experiment-code/), [experiment-design](../experiment-design/) - Downstream: [table-generation](../table-generation/), [figure-generation](../figure-generation/), [backward-traceability](../backward-traceability/) - See also: [math-reasoning](../math-reasoning/)