Bootstrapping (statistics)
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In statistics, bootstrapping is a method for estimating the sampling distribution of an estimator by resampling with replacement from the original sample. It is distinguished from the jackknife procedure, used to detect outliers, and cross-validation, used to make sure that results are repeatable. There are more complicated bootstraps for sampling without replacement, two-sample problems, regression, time series, hierarchical sampling, mediation analyses, and other statistical problems.
Bootstrapping [1] [2] is becoming the most popular method of testing mediation because it does not require the normality assumption to be met, and because it can be effectively utilized with smaller sample sizes (N < 20). However, mediation continues to be (perhaps inappropriately) most frequently determined using (1) the logic of Baron and Kenny [3] or (2) the Sobel test.
For more details see bootstrap resampling.
