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Activity Number: 125 - SPEED: Modernization of What, How, and Where We Teach Statistics Part 1
Type: Contributed
Date/Time: Monday, July 29, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics and Data Science Education
Abstract #306364 Presentation
Title: Active-Learning for Bayesian Inference: An Introductory Exercise Using MandM's Candy
Author(s): Gwendolyn Marie Eadie* and Daniela Huppenkothen and Aaron Springford and Tyler McCormick
Companies: University of Washington and University of Washington and Weyerhaeuser and University of Washington
Keywords: active learning; Bayesian; education; eliciting priors

Using m&m's candy as a teaching tool for classes in freqentist statistics has been popular for many years, with the colour distribution of the chocolates often playing a dominant role. However, there are only a couple of exercises for Bayesian statistics that take advantage of these yummy candies. In this talk, I will describe a full-fledged active-learning exercise we developed that uses m&m's candies to introduce Bayesian inference to students in a fun (and tasty!) way. In this activity, students obtain a posterior distribution for the percentage of blue m&m's produced at the factory given a bag of m&m's they are given in class. The exercise gives them a chance to quantify their prior information about different m&m's colours before opening the data. I will describe how our exercise works in practice, in the context of the intended learning outcomes and lesson plan. This presentation may also include actual m&m's and audience participation.

Authors who are presenting talks have a * after their name.

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