Abstract

Some key ideas in statistics and probability are hard for students, including sampling distributions. Computer simulation lets students gain experience with and intuition for these concepts. Bootstrapping can reinforce that learning, and provide a way for students (and future practitioners!) to estimate sampling distributions when they have data but do not know the underlying distribution. Bootstrapping also frees us from the requirement to teach inference only for statistics for which simple formulas are available|we can bootstrap robust statistics like the median as easily as the mean. For the promise of simulation and bootstrapping to be realized, they must be available and easy to use in general-purpose statistical software, complete with the exploratory data analysis and inferential capabilities required in teaching and practice. We discuss some of the available software for simulation and bootstrapping, in particular software built on S-Plus. Key words: bootstrap, resampling, simulation,

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