The Bootstrap method, a computer-intensive statistical method of estimation, is illustrated using a simple and efficient Statistical Analysis System (SAS) routine. The utility of the method for generating unknown parameters, including standard errors for simple statistics, regression coefficients, discriminant function coefficients, and factor weights is also demonstrated. Data were drawn from the High School and Beyond Survey, a longitudinal study of high school sophomores and seniors. A sample of 500 (out of 28,240) seniors was selected. Using the SAS routine, 100, 200, and 500 Bootstrap samples were used to compute the Bootstrap estimate and its standard error for the following parameters: (1) means; (2) correlation coefficient; (3) simple regression coefficient; (4) multiple regression coefficient; (5) discriminant function coefficient; and (6) factor loadings. Research literature suggests that Bootstrap samples are used to estimate parameters and generate their standard errors and to develop non-parametric confidence intervals. The use of Bootstrap and Jackknife methods may have been limited by researchers' lack of familiarity with Monte Carlo methods or the misconception that such methods require enormous computer resources. One table lists the SAS routine, and five tables illustrate the method used. (SLD)