Best use case
statistics is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Teams using statistics should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/statistics/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How statistics Compares
| Feature / Agent | statistics | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
This skill provides specific capabilities for your AI agent. See the About section for full details.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Statistical Analysis & Quality Control ## Overview Comprehensive statistical methodology for scientific research. Covers test selection, assumption verification, power analysis, effect size reporting, and reporting standards. ## Test Selection Guide | Data Type | Groups | Paired? | Normal? | Recommended Test | |-----------|--------|---------|---------|-----------------| | Continuous | 2 | No | Yes | Independent t-test | | Continuous | 2 | No | No | Mann-Whitney U | | Continuous | 2 | Yes | Yes | Paired t-test | | Continuous | 2 | Yes | No | Wilcoxon signed-rank | | Continuous | 3+ | No | Yes | One-way ANOVA + post hoc | | Continuous | 3+ | No | No | Kruskal-Wallis + Dunn | | Continuous | 3+ | Yes | Yes | Repeated measures ANOVA | | Categorical | 2x2 | — | — | Chi-square / Fisher's exact | | Time-to-event | 2+ | — | — | Log-rank + KM curves | | Time-to-event | Adjusted | — | — | Cox proportional hazards | | Continuous | Prediction | — | — | Linear/logistic regression | ## Assumption Checks - **Normality**: Shapiro-Wilk (n < 50), Kolmogorov-Smirnov (n > 50), Q-Q plot visual - **Homoscedasticity**: Levene's test, Bartlett's test - **Independence**: study design review (not a statistical test) - **Proportional hazards**: Schoenfeld residuals, log-log plot ## Multiple Comparison Correction - **Bonferroni**: conservative, for few comparisons - **Holm-Bonferroni**: step-down, less conservative than Bonferroni - **FDR (Benjamini-Hochberg)**: for many comparisons (e.g., genomics) - **Tukey HSD**: for all pairwise comparisons after ANOVA ## Effect Size Guidelines | Measure | Small | Medium | Large | |---------|-------|--------|-------| | Cohen's d | 0.2 | 0.5 | 0.8 | | Pearson r | 0.1 | 0.3 | 0.5 | | Odds Ratio | 1.5 | 2.5 | 4.3 | | R-squared | 0.02 | 0.13 | 0.26 | ## Reporting Standards - Always report: test statistic, degrees of freedom, exact p-value, effect size, 95% CI - Follow STROBE (observational), CONSORT (RCTs), PRISMA (reviews), ARRIVE (animal) - Never say "trend toward significance" for p > 0.05 - Report non-significant results honestly
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