statistical-analysis

Run assumption checks for hypothesis testing in Python by executing the comprehensive_assumption_check script on your dataset. This generates a full diagnostic report covering normality, variance, and outlier checks to validate test prerequisites.

You can generate APA-style statistical reports from experimental data using this toolkit. It supports t-tests, ANOVA, regression, and Bayesian methods, formatting effect sizes and results into professional, publication-ready documentation.

The best way to choose the right statistical test for your research data is using this toolkit's test selection and planning feature. It guides you to appropriate tests based on your specific data characteristics and analysis requirements.

Yes, you need pandas and scipy installed to run Bayesian regression diagnostics. This Skill requires Python libraries including numpy, pandas, scipy, matplotlib, and seaborn to execute its reproducible analysis workflows.

This statistical analysis toolkit supports power analysis and effect sizes alongside Bayesian methods. It provides end-to-end analysis pipelines from data loading to interpretation, ensuring comprehensive documentation for researchers.

You should use reproducible statistical workflows for observational data to standardize and accelerate data-driven decisions. The included scripts and references provide end-to-end pipelines, ensuring consistent analysis and comprehensive documentation.