The name for this idea comes from the idiom "to pull oneself up by one's bootstraps," which connotes getting out of a hole without anything to stand on. The idea of the bootstrap is to assume, for the purposes of estimating uncertainties, that the sample is the population, then use the SE for sampling from the sample to estimate the SE of sampling from the population. For sampling from a box of numbers, the SD of the sample is the bootstrap estimate of the SD of the box from which the sample is drawn. For sample percentages, this takes a particularly simple form: the SE of the sample percentage of n draws from a box, with replacement, is SD(box)/n^½, where for a box that contains only zeros and ones, SD(box) = ((fraction of ones in box)×(fraction of zeros in box) )^½. The bootstrap estimate of the SE of the sample percentage consists of estimating SD(box) by ((fraction of ones in sample)×(fraction of zeros in sample))^½. When the sample size is large, this approximation is likely to be good.