Bootstrapping has enormous potential in statistics education and practice, but
there are subtle issues and ways to go wrong. For example, the common combination
of nonparametric bootstrapping and bootstrap percentile confidence intervals is
less accurate than using t-intervals for small samples, though more accurate for
larger samples. My goals in this article are to provide a deeper understanding of
bootstrap methods—how they work, when they work or not, and which methods work
better—and to highlight pedagogical issues. Supplementary materials for this
article are available online.