Opinion ID: 2994594
Heading Depth: 3
Heading Rank: 2

Heading: Use of Statistical Evidence

Text: In a discrimination case proceeding under a so- called disparate impact theory, statistical evidence is obviously of central importance, because in such a case the plaintiff is trying to show that a particular job requirement has a differential impact on a defined protected group. But, as the district court correctly held, disparate impact is not a theory available to age discrimination plaintiffs in this circuit. See, e.g., Maier v. Lucent Technologies, Inc., 120 F.3d 730, 735 & n.4 (7th Cir. 1997); EEOC v. Francis W. Parker School, 41 F.3d 1073, 1077-78 (7th Cir. 1994). See also Blackwell v. Cole Taylor Bank, 152 F.3d 666, 672 (7th Cir. 1998) (citing cases on both sides of issue from various circuits). We must therefore consider the ways in which statistical evidence can be brought to bear in a disparate treatment or pattern or practice case. Our general framework in this connection is set forth in the Supreme Court’s decisions about the admissibility of expert testimony, like the statistical evidence proffered here. The leading case, mysteriously not cited in the plaintiffs’ briefs, is Daubert v. Merrell Dow Pharmaceuticals, Inc., 509 U.S. 579 (1993). Daubert made clear that expert testimony should not be considered in a case unless the expert has genuine expertise (there, scientific knowledge) and that expertise will assist the trier of fact to understand or determine a fact in issue. Id. at 592. The Court elaborated on Daubert’s framework in Kumho Tire Co. v. Carmichael, 526 U.S. 136 (1999). The twin requirements for expert testimony, as it explained in Kumho Tire, are relevance and reliability. The expert must employ[ ] in the courtroom the same level of intellectual rigor that characterizes the practice of an expert in the relevant field. Id. at 152. The trial court, exercising its gatekeeping function, must examine (among other things) the expert’s qualifications, the methodologies she used, and the relevance of the final results to the questions before the jury. We review the district court’s decisions deferentially, see General Electric Corp. v. Joiner, 522 U.S. 136 (1997). At the threshold, the defendants have questioned whether statistical evidence as a whole can ever be useful in a case alleging disparate treatment or a discriminatory pattern or practice, as opposed to a disparate impact case. The short answer is yes: statistical evidence can be very useful to prove discrimination in either or both of those two kinds of cases, but it will likely not be sufficient in itself. See generally David C. Baldus and James W.L. Cole, Statistical Proof of Discrimination, sec.sec. 9.02, 9.42 (1980). The Supreme Court discussed the use of statistical evidence in a pattern or practice case in Hazelwood School District v. United States, 433 U.S. 299 (1977), in which the Court approved of the use of statistics to help show that the school district was discriminating on the basis of race in its faculty hiring decisions. See id. at 307. Hazelwood also underscored the importance of looking to the proper community or group when making statistical comparisons. The relevant labor market was a question of fact for the trial court to consider, although it was probably something like the market for school teachers in the area. Id. at 310-12. Further emphasizing the factual nature of this inquiry, the Court also held that the employer school district was entitled to an opportunity to rebut any inference of discrimination raised by the plaintiffs’ statistical showing. Id. at 309-10. The Court returned to the topic of statistics in Bazemore v. Friday, 478 U.S. 385 (1986). Bazemore involved both a pattern or practice claim brought by the United States and individual plaintiffs’ discrimination claims, all arising out of alleged racial discrimination in employment and the provision of services by the North Carolina Agricultural Extension Service. The court of appeals had rejected the plaintiffs’ statistical evidence because it had not taken into account every factor that might have affected the employees’ salary levels. The Supreme Court disagreed, holding both that the statistical study should have been admitted and that [n]ormally, failure to include variables will affect the analysis’ probativeness, not its admissibility. Id. at 400. The Court also noted that the statistical evidence had to be evaluated in the light of the remaining evidence in the record. This court, too, has had a number of opportunities to discuss the use of statistical evidence. In Radue v. Kimberly-Clark Corp., 219 F.3d 612 (7th Cir. 2000), we upheld summary judgment for an employer in an ADEA case where the employee’s prima facie case was primarily composed of statistics that showed that older employees were treated less favorably than younger employees in various RIFs the employer had carried out. But those statistics were flawed in a number of ways, and the plaintiff had little else with which to support his case. For one thing, the statistics looked at a completely different part of the company from the one in which the plaintiff worked and they involved an earlier RIF, not the one in which the plaintiff lost his job. Id. at 616. For another, the statistics failed to address the essence of the plaintiff’s claim: he did not allege that the RIF was age-based; he claimed instead that the transfers awarded in the wake of the RIFs were given preferentially to younger employees. Id. Finally, the court noted that the statistics standing alone could not prove causation; they could only show a relation between the employer’s decisions and the affected employees’ traits. Id. See also Tagatz v. Marquette Univ., 861 F.2d 1040, 1044 (7th Cir. 1988) (Correlation is not causation.). There is no presumption that statistical evidence has no useful role to play in disparate treatment employment discrimination cases-- indeed, we are hard pressed to see how anyone could take such a position consistently with the Supreme Court’s guidance on the matter and this court has not done so. Thus, for example, this court in Mister v. Illinois Central Gulf Railroad Co., 832 F.2d 1427 (7th Cir. 1987), the plaintiffs successfully relied on a statistical showing that the railroad hired a much larger proportion of white applicants than African-American applicants to prove disparate treatment. The employer, ICG, had complained that the data was based on inaccurate assumptions about the labor market, but we rejected that criticism because the expert had used the best data available, which had come from ICG itself. Id. at 1430. The opinion then discussed at some length what one can learn from statistical evidence and what its limitations are. Id. at 1430-31. Studies in employment discrimination cases typically begin by defining the relevant labor market, and then ask what the results would be for the salient variable (race in Mister, age in our case) if there were no discrimination. That is called the null hypothesis. If the relevant market is 40% African-American, for instance, one would expect 40% of hires to be African-American under the null hypothesis. If the observed percentage of African-American hires is only 20%, then the statistician will compute the standard deviation from the expected norm and indicate how likely it is that race played no part in the decisionmaking. Two standard deviations is normally enough to show that it is extremely unlikely (that is, there is less than a 5% probability) that the disparity is due to chance, giving rise to a reasonable inference that the hiring was not race-neutral; the more standard deviations away, the less likely the factor in question played no role in the decisionmaking process. See Hazelwood, 433 U.S. at 308 n.14; Castaneda v. Partida, 430 U.S. 482, 496 n.17 (1977); see also Tagatz, 861 F.2d at 1044 (discussing the significance of statistical significance); Palmer v. Schultz, 815 F.2d 84, 90-97 (D.C. Cir. 1987) (discussing cases and forms of statistical proof). See generally Baldus and Cole, supra, sec. 9.03 (1980 & Supp. 1987). Mister also noted the importance of making sure that any testing adequately accounts for the real variables that the employer took into account. The plaintiffs’ evidence there was wanting because it assumed that the job applicants were identical in every respect except for race, even though other factors like physical condition, employment history, or other non-racial variables might have entered into the employer’s calculus. 832 F.2d at 1431; see also Tagatz, 861 F.2d at 1044. But, importantly, it was the ICG that had the responsibility of offering alternative explanations. The only one it actually advanced was that it was concerned about the distance its employees had to travel to get to the job site; for reasons pointed out in the opinion, this was singularly unpersuasive. (It is also important to note that Mister reached this court after a full trial on the merits; nothing in the opinion suggests that the plaintiffs’ statistical showing was so weak that summary judgment for the defendant would have been appropriate.)