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On Teaching Statistical Practice: From Novice to Expert

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  • Joel B. Greenhouse
  • Howard J. Seltman

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.

Suggested Citation

  • Joel B. Greenhouse & Howard J. Seltman, 2018. "On Teaching Statistical Practice: From Novice to Expert," The American Statistician, Taylor & Francis Journals, vol. 72(2), pages 147-154, April.
    • Handle: RePEc:taf:amstat:v:72:y:2018:i:2:p:147-154
    DOI: 10.1080/00031305.2016.1270230
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    References listed on IDEAS

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    1. Yeyi Zhu & Ladia M. Hernandez & Peter Mueller & Yongquan Dong & Michele R. Forman, 2013. "Data Acquisition and Preprocessing in Studies on Humans: What is Not Taught in Statistics Classes?," The American Statistician, Taylor & Francis Journals, vol. 67(4), pages 235-241, November.
    2. Byran J. Smucker & A. John Bailer, 2015. "Beyond Normal: Preparing Undergraduates for the Work Force in a Statistical Consulting Capstone," The American Statistician, Taylor & Francis Journals, vol. 69(4), pages 300-306, November.
    3. Robert E Kass & Brian S Caffo & Marie Davidian & Xiao-Li Meng & Bin Yu & Nancy Reid, 2016. "Ten Simple Rules for Effective Statistical Practice," PLOS Computational Biology, Public Library of Science, vol. 12(6), pages 1-8, June.
    4. C. J. Wild & M. Pfannkuch, 1999. "Statistical Thinking in Empirical Enquiry," International Statistical Review, International Statistical Institute, vol. 67(3), pages 223-248, December.
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