Document Type

Article

Publication Date

12-2015

Department

Mathematics, Statistics, and Computer Science

Keywords

bootstrap, permutation, randomization, education

Abstract

The use of simulation-based methods for introducing inference is growing in popularity for the Stat 101 course, due in part to increasing evidence of the methods ability to improve students’ statistical thinking. This impact comes from simulation-based methods (a) clearly presenting the overarching logic of inference, (b) strengthening ties between statistics and probability/mathematical concepts, (c) encouraging a focus on the entire research process, (d) facilitating student thinking about advanced statistical concepts, (e) allowing more time to explore, do, and talk about real research and messy data, and (f) acting as a firmer foundation on which to build statistical intuition. Thus, we argue that simulation-based inference should be an entry point to an undergraduate statistics program for all students, and that simulation-based inference should be used throughout all undergraduate statistics courses. In order to achieve this goal and fully recognize the benefits of simulation-based inference on the undergraduate statistics program we will need to break free of historical forces tying undergraduate statistics curricula to mathematics, consider radical and innovative new pedagogical approaches in our courses, fully implement assessment-driven content innovations, and embrace computation throughout the curriculum.

Comments

  • This is a pre-publication author manuscript of the final, published article. The definitive version is published by Taylor & Francis
  • Copyright © 2015 The American Statistician

Source Publication Title

The American Statistician

Publisher

Taylor & Francis

Volume

69

Issue

4

First Page

362

DOI

10.1080/00031305.2015.1081619

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