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Garland's Digest Treatise
on Employment Discrimination Law



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Chapter 5 - Proving non-disparate treatment claims
5.200 Disparate Impact
5.250 Evidence necessary to establish prima facie case
5.251 Statistical evidence

5.251.10 In general

2d Circuit

In EEOC v. Joint Apprenticeship Committee of Joint Industry Bd. of Elec. Industry, 186 F.3d 110, 117 (2d Cir. 1999), the court explained:

   Because statistical analysis, by its very nature, can never scientifically prove discrimination, a disparate impact plaintiff need not prove causation to a scientific degree of certainty. Bazemore v. Friday, 478 U.S. 385, 400 (1986); Ramona L. Paetzold and Steven L. Willborn, The Statistics of Discrimination, 2.05 (1996) (hereinafter "Paetzold and Willborn"). Accordingly, this Court has held that a plaintiff may establish a prima facie case of disparate impact discrimination by proffering statistical evidence which reveals a disparity substantial enough to raise an inference of causation. That is, a plaintiff's statistical evidence must reflect a disparity so great that it cannot be accounted for by chance. Waisome at 1375; Bridgeport Guardians, Inc. v. City of Bridgeport, 933 F.2d 1140, 1146 (2d Cir. 1991).

5th Circuit

In Muñoz v. Orr, 200 F.3d 291, 300 (5th Cir. 2000), the court noted:

   Claims of disparate impact under Title VII must, of necessity, rely heavily on statistical proof. See Watson v. Fort Worth Bank and Trust, 487 U.S. 977, 987 (1988).

9th Circuit

In Stout v. Potter, 276 F.3d 1118, 1122 (9th Cir. 2002), the court explained:

   A prima facie case of disparate impact is "usually accomplished by statistical evidence showing 'that an employment practice selects members of a protected class in a proportion smaller than their percentage in the pool of actual applicants.'" Robinson v. Adams, 847 F.2d 1315, 1318 (9th Cir. 1988) (quoting Moore v. Hughes Helicopters, Inc., 708 F.2d 475, 482 (9th Cir. 1983)). Although statistical data alone, in a proper case, may be adequate to prove causation, Wards Cove, 490 U.S at 650, 109 S. Ct. at 2121, the "statistical disparities must be sufficiently substantial that they raise such an inference of causation." Watson, 487 U.S. at 995, 108 S. Ct. at 2789; see also Clady v. County of Los Angeles, 770 F.2d 1421, 1428-29 (9th Cir. 1985)

10th Circuit

In Bullington v. United Air Lines, Inc., 186 F.3d 1301 (10th Cir. 1999), overruled on other grounds, the court explained:

   Ms. Bullington used a type of statistics called applicant flow data to establish her disparate impact claim. Applicant flow data, long recognized as an acceptable comparison model in discrimination cases, generally contrasts the racial or gender composition of persons who applied for the position and persons holding the at-issue jobs. See Wards Cove Packing Co., Inc. v. Atonio, 490 U.S. 642, 650-51 (1989) (recognizing that statistics measuring "otherwise qualified applicants" may be probative in disparate impact cases); Hazelwood Schl. Dist. v. United States, 433 U.S. 299, 309 n.13 (1977) (noting that applicant flow data may be "very relevant" in proving discrimination). Such data is generally considered probative because it reflects how the employer's hiring procedure actually operated. See, e.g., Ramona L. Paetzold & Steven L. Willborn, The Statistics of Discrimination, § 4.03 at 7 (1998). Of course, applicant flow data, like all statistical proof, is susceptible to distortion. Accordingly, we require the data to cross a "threshold of reliability before it can establish even a prima facie case of disparate impact." Ortega, 943 F.2d at 1243 (internal quotation marks and citation omitted, and emphasis added). The "reliability" or usefulness of any particular analysis will depend on the surrounding facts and circumstances of the case. See Watson v. Fort Worth Bank & Trust, 487 U.S. 977, 995 n.3, 997 (1988).

Bullington, 186 F.3d at 1313 (footnotes omitted).



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