Date of Original Version




Abstract or Description

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.




Authors’ Affiliation and Acknowledgments

Joel B. Greenhouse is Professor, Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213 (Email: Howard J. Seltman is Senior Research Statistician, Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213 (Email: Parts of this paper 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. We also acknowledge with gratitude the talented, dedicated and good looking students who participated in the MSP program.



Published In

The American Statistician.