Statistical Testing and APA Reporting

Problem

Statistical results are often hard to audit when test choice is wrong, assumptions are not checked, effect sizes are missing, or reports are incomplete. This skill organizes hypothesis testing, regression, correlation, Bayesian analysis, and APA-style reporting into an executable workflow.

How It Works

• State the question first: define hypotheses, outcomes, predictors, and design before touching data, reducing accidental p-hacking.
• Testing and diagnostics: covers independent/paired t-tests, Mann-Whitney U, ANOVA, Kruskal-Wallis, chi-square, Fisher exact, Pearson/Spearman correlation, linear/logistic regression, and Bayesian alternatives.
• Assumption checks: uses assumption_checks.py for normality, variance homogeneity, linearity, outliers, and regression diagnostics; suggests Welch, nonparametric, or robust standard error fixes.
• Reporting: structures descriptive statistics, exact p values, effect sizes with CIs, assumption checks, and all planned analyses.

Boundaries

Best for experimental or observational data and research writing. Model-specific APIs such as ARIMA or complex PyMC workflows may need companion skills. It emphasizes reproducibility and does not replace domain judgment; small samples still require power and prior consideration.