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Statistical Analysis

Maintainer K-Dense Inc. · Last updated March 31, 2026

Statistical analysis is a systematic process for testing hypotheses and quantifying relationships. Conduct hypothesis tests (t-test, ANOVA, chi-square), regression, correlation, and Bayesian analyses with assumption checks and APA reporting. Apply this skill for academic research.

Claude CodeOpenClawNanoClawAnalysisWritingstatistical-analysisanalysisanalysis & methodologystatistical analysis

Original source

K-Dense-AI/claude-scientific-skills

https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/statistical-analysis

Maintainer
K-Dense Inc.
License
MIT license
Last updated
March 31, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

  • Statistical analysis is a systematic process for testing hypotheses and quantifying relationships. Conduct hypothesis tests (t-test, ANOVA, chi-square), regression, correlation, and Bayesian analyses with assumption checks and APA reporting. Apply this skill for academic research.
  • results = comprehensive_assumption_check( data=df, value_col='score', group_col='group', # Optional: for group comparisons alpha=0.05 ).
  • This performs: 1. Outlier detection (IQR and z-score methods) 2. Normality testing (Shapiro-Wilk test + Q-Q plots) 3. Homogeneity of variance (Levene's test + box plots) 4. Interpretation and recommendations.

Source Doc

Excerpt From SKILL.md

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

1. Test Selection and Planning

  • Choose appropriate statistical tests based on research questions and data characteristics
  • Conduct a priori power analyses to determine required sample sizes
  • Plan analysis strategies including multiple comparison corrections

2. Assumption Checking

  • Automatically verify all relevant assumptions before running tests
  • Provide diagnostic visualizations (Q-Q plots, residual plots, box plots)
  • Recommend remedial actions when assumptions are violated

Use cases

  • Conducting statistical hypothesis tests (t-tests, ANOVA, chi-square).
  • Performing regression or correlation analyses.
  • Running Bayesian statistical analyses.
  • Checking statistical assumptions and diagnostics.

Not for

  • Do not rely on this catalog entry alone for installation or maintenance details.

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