Understanding Bootstrap Confidence Interval Output from the R boot Package

Nuances of Bootstrapping Most applied statisticians and data scientists understand that bootstrapping is a method that mimics repeated sampling by drawing some number of new samples (with replacement) from the original sample in order to perform inference. However, it can be difficult to understand output from the software that carries out the bootstrapping without a more nuanced understanding of how uncertainty is quantified from bootstrap samples. To demonstrate the possible sources of confusion, start with the data described in Efron and Tibshirani’s (1993) text on bootstrapping (page 19).
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