statistical-analysis

Statistical analysis is guided by loading your dataset and running a comprehensive assumption check on target variables to initiate automated normality, variance, and linearity diagnostics before test selection.

Test selection guidance covers t-tests, ANOVA, regression, chi-square, and Bayesian alternatives, matching the appropriate statistical test to your experimental or observational dataset based on hypothesis testing requirements and assumption check results.

Power analysis and effect-size reporting are supported alongside pandas and scipy dependencies, enabling you to plan studies and interpret results by calculating required sample sizes and statistical power for your hypothesis testing.

Bayesian methods are supported as alternatives to traditional hypothesis testing, providing a structured framework to apply Bayesian statistical analysis across your datasets when standard frequentist approaches face assumption violations or interpretation limits.

Reproducibility in statistical analysis is ensured through frontmatter metadata, references, and optional Python scripts, creating reproducible workflows that document test selection, assumption checks, and APA-formatted reporting outputs.