statistical-analysis
Conduct rigorous statistical tests—t-tests, ANOVA, chi-square, correlation, regression, and Bayesian methods—with systematic assumption verification and effect size reporting. The skill walks you through test selection, data inspection, assumption diagnostics, and APA-style write-ups so your analysis withstands peer review.
Statistical Analysis guides you through hypothesis testing, assumption checking, and APA-formatted reporting for research data.
AI-generated summary based on this skill's SKILL.md
Decision gist · record as of 2026-07-28
Statistical Analysis guides you through hypothesis testing, assumption checking, and APA-formatted reporting for research data. Conduct rigorous statistical tests—t-tests, ANOVA, chi-square, correlation, regression, and Bayesian methods—with systematic assumption verification and effect size reporting. The skill walks you through test selection, data inspection, assumption diagnostics, and APA-style write-ups so your analysis withstands peer review.
Use it when
- statistical-analysis emphasizes verifying assumptions before interpreting results.
- statistical-analysis computes effect sizes (Cohen's d, eta-squared, Cramér's V) and confidence intervals for your chosen test.
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K-Dense-AI/scientific-agent-skills/statistical-analysis · repository language: Python
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Frequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
How do I run a t-test in statistical-analysis?
statistical-analysis guides you through t-test selection and execution. Start by checking normality assumptions with Shapiro-Wilk or Q-Q plots. For equal variances, use Student's t-test; for unequal variances, use Welch's t-test. The skill helps you calculate the test statistic, p-value, effect size (Cohen's d), and confidence intervals, then format results in APA style with complete methodology documentation.
What should I check before running ANOVA?
statistical-analysis emphasizes verifying assumptions before interpreting results. Before ANOVA, check normality in each group (Shapiro-Wilk test), homogeneity of variance (Levene's test), and independence of observations. If violations occur, the skill guides you toward alternatives like Kruskal-Wallis (non-parametric) or robust methods. Post-hoc tests like Tukey HSD handle multiple comparisons after significant ANOVA results.
How does statistical-analysis calculate effect size and confidence intervals?
statistical-analysis computes effect sizes (Cohen's d, eta-squared, Cramér's V) and confidence intervals for your chosen test. These quantify practical significance beyond p-values. The skill also performs power analysis to determine required sample size before data collection or post-hoc power after analysis, ensuring your study design and interpretation meet research standards.
Can statistical-analysis handle non-parametric tests?
Yes. statistical-analysis covers Mann-Whitney U for independent groups, Wilcoxon signed-rank for paired data, and Kruskal-Wallis for multiple groups when normality or other assumptions fail. The skill guides assumption violation diagnosis and recommends appropriate non-parametric alternatives, then reports findings with effect sizes and confidence intervals in APA format.
Does statistical-analysis support Bayesian analysis?
statistical-analysis includes Bayesian methods to obtain posterior distributions and credible intervals. This approach complements frequentist hypothesis testing by quantifying uncertainty around parameters. The skill helps you specify priors, interpret posteriors, and report Bayesian findings alongside traditional statistics for comprehensive inference.
How do I report statistical findings in APA format?
statistical-analysis provides templates and examples for APA-style reporting. Include test name, test statistic, degrees of freedom, p-value, effect size, and confidence interval. The skill documents your complete methodology—sample size, assumptions checked, violations addressed, and post-hoc tests applied—so your analysis withstands peer review and meets publication standards.
SKILL.md
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Statistical Analysis
Overview
Conduct hypothesis tests (t-tests, ANOVA, chi-square), regression, correlation, and Bayesian analyses with systematic assumption checking, effect sizes, and APA-style reporting. The goal is an analysis a reviewer could not tear apart: the right test, verified assumptions, honest effect sizes, and a complete write-up.
When to Use This Skill
Use this skill when: - Conducting statistical hypothesis tests (t-tests, ANOVA, chi-square, non-parametric) - Performing regression or
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skills/statistical-analysis/SKILL.md
skills/statistical-analysis/references/assumptions_and_diagnostics.md
skills/statistical-analysis/references/bayesian_statistics.md
skills/statistical-analysis/references/effect_sizes_and_power.md
skills/statistical-analysis/references/reporting_standards.md
skills/statistical-analysis/references/test_selection_guide.md
skills/statistical-analysis/scripts/assumption_checks.py
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