A jack knife is a pocket knife that is put to many tasks, because it's ready to hand. Often there could be a better tool for the job, such as a screwdriver, a scraper, or a can-opener, but these are not usually pocket items. In statistical terms, the expression implies making do with what's available. Another simile, of an extreme situation, is the bootstrap: extricating oneself from a predicament by the only means available. Bootstraps are more empirical than other statistical processes. Given a suitable sample, many population features can be derived from the sample alone. The process takes repeated random samples from the original data. The bootstrap method is flexible and robust, well suited for analysis of data whose population distribution is uncertain, as is often the case in biological studies. For example, assumptions about distribution can be avoided when comparing quanta at synapses. There may be occasions when the bootstrap can fail: for example it is not good with extreme distributions, or to estimate statistics--like the maximum--that depend on very small features of the data. Modern computers make the tedious procedure of repeated sampling straightforward. However, standard textbooks and the standard statistics packages have failed to acknowledge the value of the bootstrap approach. (Contains 4 figures.)