The Importance of Discussing Assumptions when Teaching Bootstrapping

TL;DR

This paper emphasizes the importance of discussing assumptions in teaching bootstrap methods, providing simulations to help students understand these assumptions, especially for those with non-mathematical backgrounds.

Contribution

It highlights the need to teach bootstrap assumptions explicitly and offers simulations as educational tools to improve understanding among diverse student populations.

Findings

Simulations effectively illustrate bootstrap assumptions.

Explicit discussion of assumptions improves student understanding.

Accessible teaching methods benefit students with math anxiety.

Abstract

Bootstrapping and other resampling methods are increasingly appearing in the textbooks and curricula of courses that introduce undergraduate students to statistical methods. In order to teach the bootstrap well, students and instructors need to be aware of the assumptions behind these intervals. In this article we discuss important assumptions about simple non-parametric bootstrap intervals and their corresponding hypothesis tests. We present simulations that instructors can use to help students understand some of the assumptions behind these methods. The simulations will be especially relevant to instructors who desire to increase accessibility for students from non-mathematical backgrounds, including those with math anxiety.