By Njesa Totty
Information
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. The lesson is designed for small 100- or 200-level statistics courses with about 10-15 students who have access to a personal computer, or a desktop in a lab setting. We also discuss a formative assessment for checking student understanding of the material. The assessment allows students to implement their own simulations using a provided template.