Although item bias statistics are widely recommended for use in test development and test analysis work, problems arise in their interpretation. The purpose of the present research was to evaluate the validity of logistic test models and computer simulation methods for providing a frame of reference for item bias statistic interpretations. Specifically, the intent was to produce simulated sampling distributions of item bias statistics under the hypothesis of no bias for use in determining cut-off points to provide guidelines for interpreting item bias statistics obtained with actual test data. The test data used were the item scores of 207 white and 730 black Cleveland (Illinois) ninth graders to the 75 items on the 1985 Cleveland Reading Competency Test. The area, root mean squared difference, and Mantel-Haenszel methods were used to statistically analyze the data. The results support the basic data simulation approach used in this study. Real and simulated distribution for three item bias statistics when bias was not present were very similar and the minor differences that were found between the distributions had little effect on the interpretations of item bias statistics obtained with actual test data. Seven steps for applying the method of computer-simulated baseline statistics in test development settings are outlined. One data table and four graphs conclude the document. (Author/SLD)