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

• Upstream: experiment-code, experiment-design

• Downstream: table-generation, figure-generation, backward-traceability

• See also: math-reasoning