statistical-analysis

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---
name: statistical-analysis
description: Guided statistical analysis for research data - test selection, assumption checking, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting. Use whenever a user wants to compare groups, test a hypothesis, analyze experimental or survey data, check statistical assumptions, compute required sample sizes, or write up results - even if they never name a specific test. Covers t-tests, ANOVA, chi-square, correlation, regression, non-parametric and Bayesian methods. For low-level model APIs, see the statsmodels and pymc skills.
license: MIT license
metadata:
  version: "1.1"
  skill-author: K-Dense Inc.
---

# Statistical Analysis

## Overview

Conduct hypothesis tests (t-tests, ANOVA, chi-square), regression, correlation, and Bayesian analyses with systematic assumption checking, effect sizes, and APA-style reporting. The goal is an analysis a reviewer could not tear apart: the right test, verified assumptions, honest effect sizes, and a complete write-up.

## When to Use This Skill

Use this skill when:
- Conducting statistical hypothesis tests (t-tests, ANOVA, chi-square, non-parametric)
- Performing regression or correlation analyses
- Running Bayesian statistical analyses
- Checking statistical assumptions and diagnostics
- Calculating effect sizes and conducting power analyses
- Reporting statistical results in APA format
- Analyzing experimental or observational data for research

---

## Installation

Use **uv** to install the libraries used in this skill. Pin versions in production; unpinned installs are fine for exploration.

```bash
# Core frequentist stack (Python 3.10+; 3.12+ recommended for latest SciPy/ArviZ)
uv pip install "pingouin>=0.6" "scipy>=1.11" "statsmodels>=0.14.6" pandas matplotlib seaborn

# Bayesian modeling (PyMC 5 + ArviZ)
uv pip install "pymc>=5.0" "arviz>=1.0"
```

**Compatibility notes (verified against pingouin 0.6.1, statsmodels 0.14.6, arviz 1.2, 2026):**

- **Pingouin 0.6.0** renamed output columns to remove special characters: `p_val`, `cohen_d`, `CI95`, `p_unc` (previously `p-val`, `cohen-d`, `CI95%`, `p-unc` in 0.5.x). Examples below use the current names; if stuck on 0.5.x, use the hyphenated forms.
- **statsmodels + SciPy**: use `statsmodels>=0.14.6` with `scipy>=1.11` to avoid `_lazywhere` import errors on SciPy 1.16+.
- **ArviZ 1.x**: `az.summary()` now defaults to **89% intervals** (`eti89` columns) and the width parameter is `ci_prob` (not `hdi_prob`). To report a conventional 95% credible interval, pass `az.summary(trace, ci_prob=0.95)`.
- **One-sided Bayes Factors are gone from Pingouin**: `pg.ttest(..., alternative='greater')` silently drops the `BF10` column, and `pg.bayesfactor_ttest` raises on one-sided alternatives. For one-sided Bayesian tests, use PyMC directly (compute the posterior probability of the directional hypothesis) or JASP/R's BayesFactor.

For model-specific APIs (OLS, GLM, ARIMA), see the **statsmodels** skill. For PyMC workflows, see the **pymc** skill.

---

## Analysis Workflow

Every sound analysis follows the same arc. Skipping steps is how analyses end up retracted, so work through them in order and say what you did at each one.

1. **Frame the question before touching the data.** State the hypothesis, the outcome and predictor variables, and the design (independent vs. paired, number of groups). Commit to a planned test now — choosing the test after peeking at results is p-hacking, even when done innocently.
2. **Inspect the data.** Per group: n, mean, SD, median, missing values. Plot the raw data (histograms or box plots) before any test. Unequal group sizes, missingness, floor/ceiling effects, and outliers all change what test is appropriate — surface them to the user rather than silently working around them.
3. **Select the test** using the quick reference below, or `references/test_selection_guide.md` for designs beyond the basics (counts, time-to-event, reliability, factorial).
4. **Che