statistical-analysis

To generate APA-formatted statistical reports, this Skill executes parametric and non-parametric tests using scipy and statsmodels, then automatically formats the results, effect sizes, and visualizations into publication-ready text and tables.

Choosing the right hypothesis test is handled by the Skill's guided test selection feature, which recommends appropriate statistical methods based on your specific research questions and data types.

Yes, you can run Bayesian analysis for academic research using pymc and arviz, supported alongside traditional parametric tests like t-tests, ANOVA, and regression within the same statistical workflow.

Checking statistical assumptions before an ANOVA is automated by the Skill, which verifies data requirements, generates visualizations, and provides recommendations to ensure your analysis is valid.

Yes, this statistical analysis tool supports power analysis, allowing you to calculate the required sample sizes and detect effect sizes to ensure your research is statistically robust.

The best way to compare multiple experimental groups is using ANOVA, which this Skill guides you through by checking assumptions, running the analysis, and providing an APA-formatted report with effect sizes.