What Can Bootstrap Resampling Do, and What Should It Not Be Used For?
Many early adopters
of the procedure saw bootstrap analysis as a panacea for small or
biased samples. They reasoned that, with enough resampled data sets,
the bias and small sample would be compensated for, and would provide
a better estimate of the population parameters than the original sample
by itself. These scholars wanted to replace an estimate produced by
the sample with the average bootstrapped statistic.
Unfortunately, bootstrapped statistics are not immune to all bias. Osborne’s (2015) experiments with logistic regression suggest that results from small or biased samples tend not to be self-correcting and instead lead to promulgating bias. In other words, the averaged ...
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