statistical-analysis

Statistical hypothesis testing requires verifying assumptions like normality and homogeneity of variance before running parametric tests. This skill automates assumption checks and executes robust statistical pipelines using scipy, statsmodels, and pingouin.

APA-style reporting formats statistical summaries and effect size interpretations into publication-ready text. This skill automates the generation of professional APA-formatted results directly from your experimental data analysis workflows.

Bayesian hypothesis testing evaluates probability distributions for experimental data using pymc and arviz. This skill supports Bayesian analysis workflows alongside parametric tests and regression to provide comprehensive statistical modeling.

Yes, pandas and numpy are required dependencies for data manipulation and numerical operations. The statistical analysis pipelines also depend on scipy, statsmodels, pingouin, matplotlib, and seaborn to execute robust testing and visualization.

Yes, comparing multiple experimental groups is a core use case. The skill performs comprehensive parametric tests, regression analysis, and automated effect size calculations to interpret differences between experimental group conditions.

Automated diagnostic visualization uses matplotlib and seaborn to plot statistical test results and assumption checks. This skill generates diagnostic plots automatically during the hypothesis testing pipeline to visualize data distributions and model fit.