The Importance of Discussing Assumptions when Teaching Bootstrapping
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Bootstrapping and other resampling methods are progressively appearing in the textbooks and curricula of courses that introduce undergraduate students to statistical methods. Though simple bootstrap-based inferential methods may have more relaxed assumptions than their traditional counterparts, they are not quite assumption-free. Students and instructors of these courses need to be aware of differences in the performance of these methods when their assumptions are or are not met. This article details some of the assumptions that the simple bootstrap relies on when used for uncertainty quantification and hypothesis testing. We emphasize the importance of these assumptions by using simulations to investigate the performance of these methods when they are or are not met. We also discuss software options for introducing undergraduate students to these bootstrap methods, including a newly developed package.
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