At the present time, frequentist ideas dominate most statistics undergraduate programs, and the exposure to Bayesian ideas in undergraduate statistics is very limited. There are historical reasons for this frequentist dominance. Efron (1986) concluded that frequentists had captured the high ground of objectivity (p. 4). Bayesian methods have superior performance, often even outperforming frequentist procedures when evaluated under frequentist criteria. In the past, Bayesian methods were of limited practical use, since analytic solutions for the Bayesian posterior distributions were only possible in a few cases, and the numerical calculation of the posterior often was not feasible because of lack of computer power. Recent developments in computing power, and the development of Markov chain methods for sampling from the posterior have made Bayesian methods possible, even in very complicated models. It is clearly unsatisfactory for our profession that most of our students are not being introduced to the best methods available. In this paper I make a proposal for how our profession should deal with this challenge, by giving my answers to the journalistic "who, what, where, when, why, and how" questions about the place of Bayesian Statistics in undergraduate statistical education.
The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education