  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.
