A Bayesian Redesign of the First Probability/Statistics Course

2020/07/08 by Jim Albert, Albert, Jim
Computer Science · Mathematics · #Data Analysis with R #FOS: Computer and information sciences #Other Statistics (stat.OT) #Probability and Statistical Research #Statistics Education and Methodologies #stat.OT

paper · pdf · doi:10.48550/arxiv.2007.04180

arxiv created 2020/07/08 · openalex publication_date 2020/07/08 · arxiv updated 2020/07/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The traditional calculus-based introduction to statistical inference consists of a semester of probability followed by a semester of frequentist inference. Cobb (2015) challenges the statistical education community to rethink the undergraduate statistics curriculum. In particular, he suggests that we should focus on two goals: making fundamental concepts accessible and minimizing prerequisites to research. Using five underlying principles of Cobb, we describe a new calculus-based introduction to statistics based on simulation-based Bayesian computation.

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