# Good Stats Skill

> A well-structured statistical analysis skill that guides proper hypothesis testing with effect sizes and power analysis.

- **Type:** Skill
- **Install:** `agentstack add skill-williamwjhuang-ab-test-causal-inference-skills-good-skill`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [WilliamWJHuang](https://agentstack.voostack.com/s/williamwjhuang)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [WilliamWJHuang](https://github.com/WilliamWJHuang)
- **Source:** https://github.com/WilliamWJHuang/ab-test-causal-inference-skills/tree/master/skill-evaluator/tests/fixtures/good-skill

## Install

```sh
agentstack add skill-williamwjhuang-ab-test-causal-inference-skills-good-skill
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Statistical Analysis Guide

## Overview

This skill guides the agent through proper statistical hypothesis testing,
ensuring normality checks, appropriate test selection, effect size reporting,
and multiple comparison corrections.

## When to Use

Use this skill when the user wants to:
- Compare two or more groups statistically
- Test a hypothesis about differences or relationships
- Determine if an observed effect is statistically significant

## Decision Tree

```
IF comparing two groups:
  IF data is normal (Shapiro-Wilk p > 0.05) AND variances are equal:
    → Use independent samples t-test
  ELIF data is normal but variances unequal:
    → Use Welch's t-test
  ELSE (data is non-normal):
    → Use Mann-Whitney U test
    → Consider bootstrap confidence intervals

IF comparing 3+ groups:
  IF data is normal AND variances are homogeneous:
    → Use one-way ANOVA with post-hoc (Tukey HSD)
  ELSE:
    → Use Kruskal-Wallis test
    → Post-hoc: Dunn's test with Bonferroni correction
```

## Guardrails

- **REFUSE** to report only p-values. Always include effect size (Cohen's d,
  eta-squared, or odds ratio) and confidence intervals.
- **REFUSE** to approve an experiment without power analysis showing power ≥ 80%.
- **WARN** if multiple comparisons are performed without correction (Bonferroni,
  Benjamini-Hochberg, or Holm).
- **MUST NOT** claim statistical significance without reporting the full context.

## Output Format

Always report results in this structure:

```
Test: [test name]
Effect Size: [metric] = [value] ([interpretation])
95% CI: [lower, upper]
p-value: [value] (adjusted: [method])
Power: [value]
Assumptions checked: [list]
```

## Edge Cases

- **Small samples (n < 30)**: Use exact tests or bootstrap methods
- **Tied data**: Use appropriate tie-correction for rank-based tests
- **What if normality is borderline?**: Report both parametric and non-parametric results

### Common Mistakes

- Using paired tests for independent samples (anti-pattern)
- Reporting "trending toward significance" for p = 0.06 (do not use for this)
- Cherry-picking seeds for reproducibility — report mean ± std across multiple seeds

## Escape Hatch

Experienced users can override guardrails by explicitly stating:
"I acknowledge [specific guardrail] and am proceeding because [justification]."

## References

For detailed method guides, see the `references/` directory.

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [WilliamWJHuang](https://github.com/WilliamWJHuang)
- **Source:** [WilliamWJHuang/ab-test-causal-inference-skills](https://github.com/WilliamWJHuang/ab-test-causal-inference-skills)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-williamwjhuang-ab-test-causal-inference-skills-good-skill
- Seller: https://agentstack.voostack.com/s/williamwjhuang
- Browse the marketplace: https://agentstack.voostack.com/browse

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Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.