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Statistical Lessons of Ricci
The Supreme Court's decision in Ricci v. De Stefano has already garnered a great deal of attention from lawyers, political pundits, and Supreme Court watchers. While the statistical issues got almost no attention in the decision from either side, there are important statistical currents in Ricci ' as there are in any disparate impact case ' that are worthy of further attention. This two-part article focuses on three issues: 1) What do statisticians really have to say about disparate impact? 2) How might statistical analysis have played out in Ricci? and 3) Going forward, what role do statisticians have to play in the new standard (i.e., strong basis in evidence)?
To frame the discussion, it will help to lay out a few facts about the case. The City of New Haven, CT, hired a company to develop a promotional test for firefighters that would accomplish two objectives: test only skills relevant for promotion and, subject to that, minimize potential disparate impact of the results. It should be noted that under the precedent provided in Griggs v. Duke Power, 401 U.S. 424 (1971), any procedure that accomplishes the first task has a safe harbor against allegations of disparate impact, albeit a safe harbor that might well have been challenged in court on the facts.
The lieutenant's test (I focus in this paper for expository purposes only on the lieutenant's test and only on blacks and whites) was given to 43 white firefighters and 19 black firefighters, producing the following results: 25 whites passed (58%) and six blacks passed (32%). The New Haven procedure for promotion involved a second phase and the results of that phase meant that 10 whites (40% of those passing the test) and no blacks (0%) would actually receive promotions.
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