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analyzing-data skill

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This skill guides statistical analysis from test selection to APA reporting, helping researchers perform robust hypothesis tests, regressions, and power

npx playbooks add skill git-fg/thecattoolkit --skill analyzing-data

Review the files below or copy the command above to add this skill to your agents.

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SKILL.md

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---
name: analyzing-data
description: "Statistical analysis toolkit for hypothesis testing, regression, correlation, Bayesian statistics, power analysis, and APA reporting. USE when conducting academic research, analyzing experimental data, testing hypotheses with t-tests or ANOVA, performing regression analyses, calculating effect sizes, checking statistical assumptions, or generating publication-ready statistical reports. Do not use for literature reviews, tool selection, or methodology design → see conducting-research skill."
allowed-tools: [Read, Write, Edit, Bash]
---

# Statistical Analysis Protocol

## When to Use This Skill

This skill should be used when:
- Conducting statistical hypothesis tests (t-tests, ANOVA, chi-square)
- 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

---

## Core Capabilities

- **Test Selection and Planning**: Choose appropriate tests and compute power. See [references/test_selection_guide.md](references/test_selection_guide.md).
- **Assumption Checking**: Verify normality, homogeneity, etc. See [references/assumptions_and_diagnostics.md](references/assumptions_and_diagnostics.md).
- **Statistical Testing**: hypothesis testing, regression, correlation, Bayesian. See [references/analysis-examples.md](references/analysis-examples.md).
- **Effect Sizes**: Calculate and interpret. See [references/effect_sizes_and_power.md](references/effect_sizes_and_power.md).
- **Reporting**: APA-style reports. See [references/reporting_standards.md](references/reporting_standards.md).

---

## Workflow Decision Tree

Use this decision tree to determine your analysis path:

```
START
│
├─ Need to SELECT a statistical test?
│  └─ YES → See [references/test_selection_guide.md](references/test_selection_guide.md)
│  └─ NO → Continue
│
├─ Ready to check ASSUMPTIONS?
│  └─ YES → See [references/assumptions_and_diagnostics.md](references/assumptions_and_diagnostics.md)
│  └─ NO → Continue
│
├─ Ready to run ANALYSIS?
│  └─ YES → See [references/analysis-examples.md](references/analysis-examples.md)
│  └─ NO → Continue
│
└─ Need to REPORT results?
   └─ YES → See [references/report-templates.md](references/report-templates.md)
```

---

## Resources

- **[references/test_selection_guide.md](references/test_selection_guide.md)**: Decision tree for choosing tests.
- **[references/assumptions_and_diagnostics.md](references/assumptions_and_diagnostics.md)**: Guidance on assumption checks.
- **[references/effect_sizes_and_power.md](references/effect_sizes_and_power.md)**: Effect sizes and power analysis.
- **[references/bayesian_statistics.md](references/bayesian_statistics.md)**: Bayesian methods.
- **[references/reporting_standards.md](references/reporting_standards.md)**: APA-style reporting.
- **[references/analysis-examples.md](references/analysis-examples.md)**: Code examples for tests.
- **[references/report-templates.md](references/report-templates.md)**: Report templates.

### Scripts

- **[scripts/assumption_checks.py](scripts/assumption_checks.py)**: Automated assumption checking tools.

Overview

This skill is a statistical analysis toolkit for researchers conducting hypothesis tests, regression, correlation, Bayesian analyses, power calculations, and APA-style reporting. It streamlines test selection, assumption checking, effect size computation, and generation of publication-ready statistical summaries. Use it to move from raw data to defensible statistical conclusions and clear reports.

How this skill works

The skill inspects your research questions and dataset to recommend appropriate tests and power calculations, runs hypothesis tests (t-tests, ANOVA, chi-square), regression and correlation models, and performs Bayesian analyses when requested. It automates assumption checks (normality, homoscedasticity, independence), computes effect sizes and confidence intervals, and formats results into APA-style output ready for manuscripts or reports.

When to use it

When choosing and running hypothesis tests (t-tests, ANOVA, chi-square).
When performing regression, correlation, or model diagnostics for empirical data.
When conducting Bayesian analyses or reporting Bayes factors and posterior summaries.
When calculating effect sizes, confidence intervals, and conducting power analyses.
When you need APA-format tables and narrative summaries for publication or thesis reporting.

Best practices

Pre-register hypotheses and analysis plans before running confirmatory tests.
Inspect and clean data first; handle missing data and outliers before inferential tests.
Always run assumption checks and report diagnostics alongside test results.
Report effect sizes and confidence intervals, not just p-values; include power analysis where relevant.
Use Bayesian methods for flexible inference or when prior information is available; document priors and sensitivity checks.

Example use cases

Compare group means with independent or paired t-tests and report Cohen’s d and 95% CI in APA format.
Run one-way or factorial ANOVA, check homogeneity and normality, then report follow-up contrasts and effect sizes.
Fit linear or logistic regression models, check multicollinearity and residuals, and produce formatted model summaries for publication.
Conduct a power analysis to determine sample size for a planned experiment based on expected effect size and desired power.
Perform a Bayesian re-analysis of an existing result, report posterior distributions, credible intervals, and Bayes factors.

FAQ

Can this skill choose the right test for my data?

Yes. It uses a decision workflow to recommend tests based on variable types, design (paired vs independent), and sample size, and it can also run power calculations.

Does it handle assumption violations?

It runs assumption diagnostics and suggests remedies (transformations, nonparametric alternatives, robust methods) and documents diagnostic results for reporting.