Mastering Statistical Hypothesis Testing: Comparative Statistical Programming, Analysis, and Modeling for R, Python, & SAS®

This hands-on training seminar delivers a clear, comparative, and applied foundation in statistical hypothesis testing and modeling across the R, Python, and SAS® programming languages. Designed for analysts, data scientists, researchers, and professionals in clinical, healthcare, pharmaceutical, and other data-driven industries, the workshop emphasizes practical programming applications for model selection, checking assumptions, running diagnostics, and interpreting results.

Registered attendees will learn when to apply parametric vs. non-parametric tests, how to distinguish statistical significance from practical or clinical significance, and how to conduct power analyses to determine the minimum sample size needed for effective study design. Through guided exercises, participants will perform exploratory data analysis (EDA), describe and visualize datasets, build statistical models, and interpret results using:

• R (RStudio by Posit)

• Python (Jupyter Notebook)

• SAS® 9.4 (free-to-use SAS® OnDemand for Academics)

This training seminar is structured to highlight cross-language similarities and differences, giving attendees a practical roadmap for statistical analysis using Python, R, or SAS® 9.4.

Key topics include:

• Understanding statistical significance, effect size, and practical significance in experiments;

• Exploratory data analysis (EDA), summarization, and visualization across R, Python, and SAS®;

• Comparing two groups: 2-sample t-test (Welch’s) and Mann-Whitney U test;

• Comparing multiple groups: One-way ANOVA models and Kruskal-Wallis test;

• Modeling factorial ANOVA and evaluating interaction effects;

• Checking model assumptions and conducting diagnostic tests for reliability;

• Statistical power, sample size determination, and study design considerations.

All participants will receive a Certificate of Completion, non-redistributable PDF slides, documented R, Python, and SAS code examples, and the workshop dataset to continue practicing independently.