(This talk is intended for introductory statistics students on up.) 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 examples from our new supplemental chapter for introductory statistics texts, titled "Bootstrap Methods and Permutation Tests". This material will be incorporated in the next version of Moore & McCabe, Introduction to the Practice of Statistics. The bootstrap and permutation tests are also useful in their own right, not just to help learn concepts. They can be used in many places where standard methods fail, or are terribly inaccurate. In one of our examples the P-values produced by standard t-tests are off by a factor of four! I'll also demonstrate a new library that simplifies using the bootstrap and permutation tests. The library works with S-PLUS, including the free student version.
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