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.

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 Detects data types, recommends tests, runs comparisons, outputs effect sizes and significance stars. Requires numpy, scipy. Format p values 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/)

data-analysis