Why Bayesian Ideas Should Be Introduced in the Statistics Curricula and How to Do So. Issue 3 (1st September 2020)

• Record Type: Journal Article
• Authors: Hoegh, Andrew
• Is Part Of: Journal of statistics education. Volume 28:Issue 3(2020)
• Page Start: 222
• Page End: 228
• Publication Date: 2020-09-01
• Subjects: Bayesian statistics -- MCMC -- Statistics curricula; Statistics -- Study and teaching -- Periodicals; Statistics -- Study and teaching; Periodicals; 519.5071
• DOI: 10.1080/10691898.2020.1841591
• Languages: English
• ISSNs: 1069-1898

Abstract

Abstract: While computing has become an important part of the statistics field, course offerings are still influenced by a legacy of mathematically centric thinking. Due to this legacy, Bayesian ideas are not required for undergraduate degrees and have largely been taught at the graduate level; however, with recent advances in software and emphasis on computational thinking, Bayesian ideas are more accessible. Statistics curricula need to continue to evolve and students at all levels should be taught Bayesian thinking. This article advocates for adding Bayesian ideas for three groups of students: intro-statistics students, undergraduate statistics majors, and graduate student scientists; and furthermore, provides guidance and materials for creating Bayesian-themed courses for these audiences. Supplementary files for this article are available on line.