Using computer simulation data, the effect of a single global outlier in two-group classification analysis was explored in terms of the outcome variables of change in classification results (PCHNG), change in misclassification rate (MISDIF), and change in precision of misclassification rate estimation. The precision of misclassification rate estimation was assessed in terms of the standard deviation of misclassification rate estimates. The change of interest pertained to the standard deviation without an outlier and the standard deviation with an outlying observation. Four factors were considered: (1) number of outcome variables; (2) group size; (3) distance between the groups; and (4) location of the outlier. Results indicate that the presence of a global outlier increases the misclassification rate and influences the classification results. In general, the influence of a global outlier (in terms of MISDIF and PCHNG) increased as the sample size decreased. However, the presence of a global outlier did not affect the precision of misclassification rate estimation. Nine tables and three bar graphs present results of the Monte Carlo studies. There is a 12-item list of references. (Author/SLD)