statistical-analysis

What problem does it solve? Researchers often struggle to choose the correct statistical test, verify assumptions like normality and homogeneity of variance, and report results in APA format, leading to methodological errors and reviewer rejections. ## Core Features & Use Cases - Test Selection Tables: Decision tables map data characteristics (independent/paired, normal/non-normal, group count) to appropriate tests such as t-tests, ANOVA, Mann-Whitney U, Kruskal-Wallis, and regression. - Executable Python Code: Ready-to-run examples using scipy, pingouin, and numpy for assumption checks, hypothesis tests, and effect size computation (Cohen's d, partial eta squared). - APA Reporting Templates: Pre-formatted result statements for t-tests, ANOVA, and multiple regression, plus a checklist covering power analysis through final write-up. - Use Case: When writing the results section of a paper comparing three experimental groups, use this Skill to confirm one-way ANOVA is appropriate, run the analysis with effect sizes, and draft the APA-formatted paragraph. ## Quick Start Help me choose and run the right statistical test for comparing two independent groups and write the results in APA format.