Premier Analytics Consulting at the Western Users of SAS® Software (WUSS) 2026 Conference

This hands-on workshop provides a practical introduction to statistical hypothesis testing, comparative statistical programming, and reproducible analytical workflows across Python, R, and SAS. As AI-enabled analytics, automated modeling workflows, and open-source tools become more common across regulated and research environments, professionals need the statistical foundation to evaluate results, validate assumptions, interpret model behavior, and determine whether analytical conclusions are reliable. Designed for data scientists, statisticians, statistical programmers, analysts, researchers, students, and professionals working in clinical, healthcare, pharmaceutical, policy, regulatory, operational, and applied research settings, this workshop focuses on selecting appropriate statistical tests, evaluating assumptions, interpreting results, and implementing accepted hypothesis testing techniques across multiple programming environments. Attendees will gain practical experience applying parametric and nonparametric statistical testing methods that support well-defined Statistical Analysis Plans (SAPs), reproducible analytics, and defensible reporting. Through guided examples and hands-on exercises, attendees will compare how equivalent statistical workflows are implemented in Python, R, and SAS, including differences in syntax, output, diagnostics, assumptions, and interpretation. Key Topics covered in this workshop include: ➤ Exploratory data analysis (EDA), data summarization, visualization, and preprocessing across Python, R, and SAS ➤The role of hypothesis testing in SAP-driven analysis, regulated analytics, research, and AI-enabled analytical workflows ➤ Statistical significance, practical significance, clinical significance, and effect size interpretation ➤ Selecting appropriate parametric and nonparametric tests based on research questions, data structure, and model assumptions ➤ Comparing two groups using Welch s two-sample t-test and the Mann-Whitney U test ➤ Comparing multiple groups using one-way ANOVA and the Kruskal-Wallis test ➤ Factorial ANOVA models, interaction effects, model assumptions, and diagnostic checks ➤ Cross-language implementation patterns for Python, R, and SAS This workshop helps attendees move beyond running isolated statistical procedures and understand how hypothesis testing supports analytical planning, reproducible workflows, AI-enabled analytics, and defensible decision-making. By the end of this workshop, attendees will understand how to select, implement, diagnose, compare, and interpret common statistical tests across Python, R, and SAS. All registered attendees will receive non-redistributable PDF slides, fully documented Python and R notebooks, SAS programs, and workshop datasets so they can reproduce the analyses and continue practicing after the workshop.