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5.200 Disparate Impact
5.250 Evidence necessary to establish prima facie case
5.251 Statistical evidence
5.251.10 In general
EEOC v. Joint Apprenticeship Committee of Joint
Industry Bd. of Elec. Industry, 186 F.3d 110, 117 (2d Cir. 1999), the court
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).
Muñoz v. Orr, 200 F.3d 291, 300 (5th Cir. 2000), the
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).
Stout v. Potter, 276 F.3d 1118, 1122 (9th Cir. 2002), the
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)
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).