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What Teachers Should Know About the Bootstrap: Resampling in the Undergraduate Statistics Curriculum

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  • Tim C. Hesterberg

Abstract

Bootstrapping has enormous potential in statistics education and practice, but there are subtle issues and ways to go wrong. For example, the common combination of nonparametric bootstrapping and bootstrap percentile confidence intervals is less accurate than using t -intervals for small samples, though more accurate for larger samples. My goals in this article are to provide a deeper understanding of bootstrap methods—how they work, when they work or not, and which methods work better—and to highlight pedagogical issues. Supplementary materials for this article are available online.[Received December 2014. Revised August 2015]

Suggested Citation

  • Tim C. Hesterberg, 2015. "What Teachers Should Know About the Bootstrap: Resampling in the Undergraduate Statistics Curriculum," The American Statistician, Taylor & Francis Journals, vol. 69(4), pages 371-386, November.
    • Handle: RePEc:taf:amstat:v:69:y:2015:i:4:p:371-386
    DOI: 10.1080/00031305.2015.1089789
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    1. Nicholas Chamandy & Omkar Muralidharan & Stefan Wager, 2015. "Teaching Statistics at Google-Scale," The American Statistician, Taylor & Francis Journals, vol. 69(4), pages 283-291, November.
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