statistical-analysis

Choosing the right statistical test is simplified through a comprehensive guide that matches your data characteristics and research questions to appropriate hypothesis testing, regression, or correlation methods.

Yes, you can automate assumption checking for hypothesis testing using Python libraries like scipy and pingouin, which provide visual diagnostics to verify statistical assumptions.

Calculating effect sizes for an academic study is handled automatically by the analysis workflow, which computes and interprets effect sizes for different statistical tests to ensure accurate academic reporting.

The best way to generate APA-style statistical reports is using an automated analysis workflow that produces publication-ready figures and detailed reporting directly from your statistical testing results.

Yes, you need Python libraries such as scipy, statsmodels, pingouin, and pymc installed to perform statistical computations, diagnostics, and Bayesian analysis within this workflow.

Yes, this statistical analysis workflow fully supports experimental design and academic research data, applying test selection, assumption checking, and effect size calculations to your experimental datasets.