statistical-analysis

Power analysis calculates the required sample size to detect an effect at a desired significance level. This Skill performs both a priori planning and post-hoc sensitivity assessments, integrating effect size interpretation to help you determine adequate statistical power for your research.

APA-formatted reports standardize the presentation of descriptive and inferential statistics. This Skill automatically generates manuscript-ready APA-style reporting templates and examples, allowing you to export clear outputs for academic research and clinical studies.

Bayesian methods provide alternative inferential frameworks for statistical analysis. This Skill uses Python libraries like scipy, statsmodels, pingouin, and arviz to execute tests, diagnostics, and Bayesian analyses, operating within reproducible workflows.

Analyzing a factorial design involves comparing ANOVA variants to match your data's complexity. This Skill provides structured test selection guidance to help you decide between ANOVA alternatives, verify assumptions, and generate reproducible workflows for two-group comparisons.

Running hypothesis-testing workflows requires an environment with numpy, pandas, scipy, matplotlib, and seaborn installed. The Skill provides structured guidance and automated diagnostics, reducing the need for deep manual coding while ensuring reproducible statistical analysis.