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. [Received December 2014. Revised August 2015]

中文翻译：

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教师应该了解的 Bootstrap 知识：本科统计课程中的重采样

Bootstrapping 在统计教育和实践中具有巨大的潜力，但存在一些微妙的问题和出错的方式。例如，对于小样本，非参数 bootstrapping 和 bootstrap 百分位置信区间的常见组合不如使用 t 区间准确，但对大样本更准确。我在本文中的目标是更深入地了解引导程序方法——它们如何工作、何时有效以及哪些方法更有效——并强调教学问题。本文的补充材料可在线获取。[2014 年 12 月收稿。2015 年 8 月修订]