Mastering Statistical Hypothesis Testing: Comparative Statistical Programming, Analysis, and Modeling for R, Python, & SAS®
Ryan Paul Lafler
Thursday, January 15, 2026
10:00 am - 4:00 pm PT
Virtual 6-Hour Workshop (Full-Day)
$59.95
Workshop Tags
Statistics, Hypothesis Testing, ANOVA, Data Science, Python, R, SAS, Statistical Analysis, Power Analysis, Certificate, Statistical Models
Workshop Description
This 6-hour, hands-on workshop presented by Ryan Paul Lafler provides a comprehensive and comparative introduction to statistical hypothesis testing and analysis using R, Python, and SAS®. Designed for analysts, data scientists, health researchers, and professionals across industries, this workshop focuses on understanding and correctly applying statistical tests by exploring their assumptions, diagnostic checks, and interpretation of results across platforms.
Participants will learn to identify when to use parametric versus non-parametric tests, how to assess statistical versus practical significance, and how to conduct power analyses to determine minimum sample size requirements. Through guided exercises, attendees will perform exploratory data analysis, create visualizations, and implement statistical models in R (RStudio from Posit), Python (Jupyter Notebook), and SAS 9.4 (SAS OnDemand for Academics) using real-world datasets.
Key topics include:
Understanding statistical significance, effect size, and practical interpretation
Exploratory Data Analysis (EDA) and visualization in R, Python, and SAS
2-sample t-Test (Welch’s test) and Mann-Whitney U Test
One-way ANOVA and Kruskal-Wallis Test for multi-group comparison
Factorial ANOVA and testing for interaction effects
Checking model assumptions and conducting diagnostic tests
Power analysis and determining optimal sample sizes for study design
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.
Workshop Summary
Master the art of statistical hypothesis testing in this engaging, 6-hour virtual workshop that shows a comparison between R, Python, and SAS. Learn how to select and interpret the right statistical tests for your data, assess assumptions, visualize findings, and apply power analysis to plan for robust study and experimental designs. From 2-sample t-tests to factorial ANOVA, this session bridges theory and practice with hands-on exercises, cross-platform demonstrations, and expert instruction. Participants will leave with clear workflows, reusable code, and the confidence to perform rigorous statistical testing in any professional or research context.
Benefits of Enrolling in this Workshop
By enrolling in this workshop, attendees will:
Earn a Certificate of Completion from Premier Training by Premier Analytics Consulting
Gain comparative experience conducting statistical tests in R, Python, and SAS
Learn to correctly apply parametric and non-parametric tests based on data assumptions
Understand statistical significance vs. practical significance and interpret results effectively
Conduct power analysis to determine optimal sample size and strengthen experimental design
Receive slides, datasets, and fully documented scripts to continue learning independently
Intended Audience
Beginner to intermediate. Ideal for analysts, data scientists, statisticians, researchers, and students seeking a cross-language understanding of statistical hypothesis testing and analysis.
Prerequisites
No prior experience with statistical testing is required, core concepts will be introduced step-by-step.
Some familiarity with R, Python, or SAS scripting is helpful but not required.
Basic knowledge of data handling and exploratory analysis is recommended.
About the Trainer
Ryan Paul Lafler is the President, CEO, and Lead Consultant of Premier Analytics Consulting, LLC, a San Diego–based firm specializing in AI/ML solutions, applied data science, big data processing, and full-stack systems development. He partners with clients across private industry, research, and government to design scalable, open-source workflows that power big-data pipelines, custom-built full-stack systems, agentic and generative AI copilots and processes, and advanced analytics applications for real-world decision support.
Ryan brings extensive experience as a consultant, big data scientist, AI engineer, full-stack developer, and statistician. His expertise spans Python, R, SAS®, SQL, and modern JavaScript frameworks (React, Node.js, Vite), alongside applied AI/ML, deep learning, databases, and statistical software.
He holds an M.S. in Big Data Analytics (2023) and a B.S. in Statistics (2020) from San Diego State University, where he also serves as Adjunct Faculty in the Big Data Analytics Graduate Program, the Department of Mathematics and Statistics, and the Global Campus program.