On Teaching Statistical Practice: From Novice to Expert

ABSTRACT

This article introduces principles of learning based on research in cognitive science that help explain how learning works. We adapt these principles to the teaching of statistical practice and illustrate the application of these principles to the curricular design of a new master's degree program in applied statistics. We emphasize how these principles can be used not only to improve instruction at the course level but also at the program level.

KEYWORDS:

• Applied Master's degree
• Authentic practice
• Cognitive apprenticeship
• Cognitive science
• Consulting
• Instructional design

Acknowledgments

Parts of this article were presented by the first author at the inaugural Rod Little Distinguished Lecture in the Department of Biostatistics at the University of Michigan, September 2016. The authors thank Marsha C Lovett, Director of the Eberly Center for Teaching Excellence & Educational Innovation, Carnegie Mellon University for her helpful suggestions and comments on this article. The authors also acknowledge with gratitude the talented, dedicated, and good-looking students who participated in the MSP program.

Notes

2 Not surprisingly these topics align nicely with the recent recommendations of the ASA Workgroup on Master's Degrees (Bailer et al 2013).

3 We note that while this paper was in review a complementary article by Kass et al. (2016), “Ten Simple Rules for Effective Statistical Practice,” concerned with communicating the elements of effective statistical practice to researchers appeared in PLoS Computational Biology.