A Bayesian Redesign of the First Probability/Statistics Course

TL;DR

This paper proposes a Bayesian approach to redesign the introductory statistics course, emphasizing simulation-based methods to improve accessibility and reduce prerequisites, challenging traditional curriculum structures.

Contribution

It introduces a novel calculus-based curriculum centered on Bayesian simulation techniques, offering a new framework for teaching statistical inference.

Findings

Enhanced understanding of Bayesian concepts through simulation

Reduced prerequisite knowledge for introductory statistics

Potential for more accessible and research-friendly curriculum

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.