Introducing Bayesian Analysis with $\text{m&m's}^\circledR$: an active-learning exercise for undergraduates
Abstract
We present an active-learning strategy for undergraduates that applies Bayesian analysis to candy-covered chocolate \text{m&m's}^\circledR. The exercise is best suited for small class sizes and tutorial settings, after students have been introduced to the concepts of Bayesian statistics. The exercise takes advantage of the non-uniform distribution of \text{m&m's}^\circledR~ colours, and the difference in distributions made at two different factories. In this paper, we provide the intended learning outcomes, lesson plan and step-by-step guide for instruction, and open-source teaching materials. We also suggest an extension to the exercise for the graduate-level, which incorporates hierarchical Bayesian analysis.
Cite
@article{arxiv.1904.11006,
title = {Introducing Bayesian Analysis with $\text{m&m's}^\circledR$: an active-learning exercise for undergraduates},
author = {Gwendolyn Eadie and Daniela Huppenkothen and Aaron Springford and Tyler McCormick},
journal= {arXiv preprint arXiv:1904.11006},
year = {2019}
}
Comments
Accepted to the Journal of Statistics Education (in press); 15 pages, 7 figures