Introducing Bayesian Analysis With m&m's®: An Active-Learning Exercise for Undergraduates

Abstract

We present an active-learning strategy for undergraduates that applies Bayesian analysis to candy-covered chocolate m&m’s®. 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 nonuniform distribution of m&m’s® colors, 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.

Keywords

• active-learning
• bayesian methods
• education
• eliciting priors
• inference