Statistical concepts such as sampling distributions, standard errors,
and P-values are difficult for many students. It is hard to get
hands-on experience with these abstract concepts. I think a good way
to get that experience is using bootstrapping and permutation
tests. I'll demonstrate using a variety of examples.
Though bootstrapping has enormous potential in statistics education and
practice, 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 talk 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.