The Bootstrap was originally developed from a much earlier technique called the Jackknife, invented by the brilliant and practical statistician John Wilder Tukey (1915-2000). The Jackknife was used to review the robustness of a statistic calculated from a set of data. A Jackknife value was the statistic of interest calculated with the ith value removed from the data set and is given the notation \widehat{\theta}_{(i)}. With a data set of n values, one thus has n Jackknife values, the distribution of which gives a feel for the uncertainty one has about the true value of the statistic. We say "gives a feel" because the reader is certainly not recommended to use the Jackknife as a method for obtaining any precise estimate of uncertainty. The Jackknife turns out to be an awful estimation of uncertainty.
Example:
The Jacknife model performs a Jackknife analysis on a data set in an attempt to evaluate the uncertainty about the population's mean and standard deviation. A non-parametric Bootstrap is performed on the same data set, and the results of the two techniques are shown below. The Jackknife clearly provides a distribution with far too little spread.
The links to the Jacknife software specific models are provided here:
