The wide availability of large amounts of inexpensive computing power has encouraged statisticians to explore many approaches to a basis for inference. This paper presents one such "computer-intensive" approach: the bootstrap of Bradley Efron. This methodology fits between the cases where it is assumed that the form of the distribution is known and cases where the sample provides no information about the form of the population distribution. A series of samples are drawn, randomly and with replacement. From each sample, the value of a parameter is estimated. The bootstrap conjecture is that the sampling distribution of the statistic being studied and sampling distribution found from this iterative process are essentially indentical for a wide variety of statistics. Some areas of application for this approach include regression and fixed effects analysis of variance, the median general linear model, the repeated measures design, and errors of measurement. (BW)