In a jack-knife analysis, given a sample of observations and a parameter to evaluate, a subsample is made by eliminating a proportion of the original **data** and the parameter is calculated for the subsample. This procedure is repeated n times and summarized. Since the introduction of the jack-knife (Quenouille 1949 ), researchers have used it, to defi ne limits of confi dence in many sorts of analyses, from statistics (Efron 1979 ;Smith and van Belle 1984 ) and ecology (Crowley 1992 ) to phylogeny. It has been used not only as a measure of support (Lanyon 1987 ), but as a way to obtain the best solution for large **data** sets (Farris et al. 1996 ), to test competing hypotheses (Miller 2003 ), to generalize the performance of predictive models or for cross-validation to estimate the bias of a estimator. As the bootstrapping, it could be seen as "a measure of robustness of the estimator with regard to small changes in the data" (Holmes 2003 ).