Bell Curve

626 Appendix 5 Appendix 5 627If for example, blacks do better in school than whites after choosingblacks and whites with equal test scores, we could say that the test wasbiased against blacks in academic prediction. Similarly, if they do betteron the job after choosing blacks and whites with equal test scores,the test could be considered biased against blacks for predicting workperformance. This way of demonstrating bias is tantamount to showingthat the regression of outcomes on scores differs for the two groups. Ona test biased against blacks, the regression intercept would be higher forblacks than whites, as illustrated in the graphic below. Test scores un-When a test is biased because it systematically underpredicts onegroup's performanceOutcome measureLow Low I l+ HighPredictorder these conditions would underestimate, or "underpredict," the per.formance outcome of blacks. A randomly selected black and white withthe same 1Q (shown by the vertical broken line) would not have equaloutcomes; the black would outperform the white (as shown by the horizontalbroken lines). The test is therefore biased against blacks. On anunbiased test, the two regression lines would converge because theywould have the same intercept (the point at which the regression linecrosses the vertical axis).But the graphic above captures only one of the many possible manifestationsof predictive bias. Suppose, for example, a test was less validfor blacks than for whites."' In regression terms, this would translate intoa smaller coefficient (slope in these graphics), which could, in turn, beassociated either with or without a difference in the intercept. The nextfigure illustrates a few hypothetical possibilities.All three black lines have the same low coefficient; they vary onlyWhen a test is biased because it is a less valid predictor of performancefor one group than anotherOutcome measureHighLow ILowPredictor- Black- White+ Highin their intercepts. The gray line, representing whites, has a higher coefficient(therefore, the line is steeper). Begin with the lowest of thethree black lines. Only at the very lowest predictor scores do blacks scorehigher than whites on the outcome measure. As the score on the predictorincreases, whites with equivalent predictor scores have higheroutcome scores. Here, the test bias is against whites, not blacks. For theintermediate black line, we would pick up evidence for test bias againstblacks in the low range of test scores and bias against whites in the highrange. The top black line, with the highest of the three intercepts, wouldaccord with bias against blacks throughout the range, but diminishingin magnitude the higher the score.Readers will quickly grasp that test scores can predict outcomes differentlyfor members of different groups and that such differences mayjustify claims of test bias. So what are the facts? Do we see anything likethe first of the two graphics in the data-a clear difference in intercepts,to the disadvantage of blacks taking the test? Or is the picture cloudieramixture of intercept and coefficient differences, yielding one sort ofbias or another in different ranges of the test scores? When questionsabout data come up, cloudier and murkier is usually a safe bet. So let usstart with the most relevant conclusion, and one about which there isvirtual unanimity among students of the subject of predictive bias intesting: No one has found statistically reliabk evidence of predictive biasagainst blacks, of the sort illustrated in the first graphic, in large, representativesampks of blacks and whites, where cognitive ability tests are the pedictorvariable for educational achievement or job performance. In the notes,