statistical-analysis

This skill provides a structured five-step pipeline for delivering publication-ready statistical analyses. It guides you through data auditing, assumption validation, test execution with effect sizes, interpretation, and client-ready reporting in APA format across SPSS, R, and Python.

Statistical Analysis runs a complete pipeline covering data auditing, assumption checks, hypothesis testing with effect sizes, and APA-formatted reporting.

AI-generated summary based on this skill's SKILL.md

★ 554 ⁮ 75 MIT updated 2026-07-27 by winstonkoh87

Install

winstonkoh87/Athena-Public/statistical-analysis · repository language: Python

Manual (git clone) CLI (skillfed)coming soon

git clone https://github.com/winstonkoh87/Athena-Public
cp -r Athena-Public/examples/skills/research/statistical-analysis ~/.claude/skills/statistical-analysis

Frequently asked questions

AI-generated answers based on this skill's SKILL.md and metadata

How do I check assumptions before running statistical tests?

statistical-analysis guides you through assumption validation as a critical second step in its five-step pipeline. Before executing any test, the skill walks you through checking normality (Shapiro-Wilk test), homogeneity of variance (Levene's test), multicollinearity (VIF in regression), and independence assumptions specific to your chosen test. This structured approach prevents invalid conclusions and ensures your results are defensible.

What does statistical-analysis include for APA format reporting?

statistical-analysis provides templates and guidance for generating client-ready statistical reports that comply with APA formatting standards. The skill ensures your deliverables include effect sizes, confidence intervals, test statistics, p-values, and plain-language interpretations—all structured to meet publication and professional presentation requirements across SPSS, R, and Python outputs.

Can statistical-analysis help with Cronbach's alpha reliability analysis?

Yes. statistical-analysis covers reliability testing including Cronbach's alpha as part of its comprehensive pipeline. The skill guides you through calculating internal consistency, interpreting alpha coefficients, and reporting reliability findings in client-ready format. This is essential for validating survey instruments before proceeding to main analyses.

How does statistical-analysis handle survey data quality and missing data?

statistical-analysis begins with data auditing as its first step, including assessment of missing data patterns (MCAR, MAR, MNAR), outlier detection using z-score methods, and data quality checks. This foundation ensures your survey data is clean and ready for statistical testing, preventing downstream analysis errors and supporting defensible conclusions.

What statistical tests and effect sizes does statistical-analysis cover?

statistical-analysis provides a complete pipeline covering correlation analysis, t-tests with effect size calculation, ANOVA with assumption checking, chi-square tests for independence, and multiple regression with multicollinearity diagnostics (VIF, Durbin-Watson). Each test includes effect size reporting and interpretation guidance aligned with APA standards.

Is statistical-analysis available for SPSS, R, and Python?

Yes. statistical-analysis delivers its five-step structured pipeline across SPSS, R, and Python, so you can execute the same rigorous workflow in your preferred platform. The skill ensures consistent assumption checking, effect size calculation, and APA-compliant reporting regardless of which statistical software you use.

SKILL.md

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