Good Stats Skill

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
• Source: WilliamWJHuang/ab-test-causal-inference-skills
• License: MIT

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