Resampling, Repeated Measures Designs, and You by Dr. Max Kuhn. Visit https://rstats.ai/nyr/ to learn more. Abstract: What happens when Bayesian (or non-Bayesian models) for multi-level and repeated measures designs are resampled using a full leave-subject-out scheme? Is the basic out-of-sample error consistent with the model-based estimate? Bio: Max Kuhn is a software engineer at RStudio. He is currently working on improving R’s modeling capabilities. He was a Director of Nonclinical Statistics at Pfizer Global R&D in Connecticut. He was applying models in the pharmaceutical and diagnostic industries for over 18 years. Max has a Ph.D. in Biostatistics. Max is the author of eight R packages for techniques in machine learning and reproducible research and is an Associate Editor for the Journal of Statistical Software. He, and Kjell Johnson, wrote the book Applied Predictive Modeling, which won the Ziegel award from the American Statistical Association, which recognizes the best book reviewed in Technometrics in 2015. He has taught many courses on modeling, including many classes for Predictive Analytics World, useR!, the Open Data Science Conference, the India Ministry of Information Technology, and others. Twitter: https://twitter.com/topepos Presented at the 2020 R Conference | New York (August 15th, 2020)