Introducing Bayesian Analysis with $\text{m&m's}^\circledR$: an active-learning exercise for undergraduates

Gwendolyn Eadie; Daniela Huppenkothen; Aaron Springford; Tyler McCormick

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arXiv · 1904.11006

Introducing Bayesian Analysis with $\text{m&m's}^\circledR$: an active-learning exercise for undergraduates

Gwendolyn Eadie·Daniela Huppenkothen·Aaron Springford·Tyler McCormick

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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.

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Gwendolyn Eadie, Daniela Huppenkothen, Aaron Springford, Tyler McCormick. 2019-04-16. Introducing Bayesian Analysis with $\text{m&m's}^\circledR$: an active-learning exercise for undergraduates. https://arxiv.org/abs/1904.11006

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