Bootstrap

  As a general term, bootstrapping describes any operation which allows a system to generate itself from its own small well-defined subsets (e.g. compilers, software to read tapes written in computer-independent form). The word is borrowed from the saying pull yourself up by your own bootstraps . In statistics, the bootstrap is a method allowing one to judge the uncertainty of estimators obtained from small samples, without prior assumptions about the underlying probability distributions. The method consists of forming many new samples of the same size as the observed sample, by drawing a random selection of the original observations, i.e. usually introducing some of the observations several times. The estimator under study (e.g. a mean, a correlation coefficient) is then formed for every one of the samples thus generated, and will show a probability distribution of its own. From this distribution, confidence limits can be given. For details, see [Efron79] or [Efron82]. A similar method is the jackknife.