'Dagger Through The Heart of Statistics' in Reduction-In-Force Cases

In a post on February 4, 2010, Jonathan Hyman, a partner at Kohrman Jackson & Krantz and author of "Ohio Employer's Law Blog", discussed the Sixth Circuit's decision in Schoonmaker v. Spartan Graphics Leasing. Mr. Hyman states that this decision "clarifies what a laid-off employee has to prove to establish age discrimination following a reduction in force."

In his post, Mr. Hyman states:

In Schoonmaker v. Spartan Graphics Leasing, the plaintiff claimed that the fact her employer retained younger employees in her position, and that her employer RIFed the two oldest employees, satisfied the "additional evidence" necessary to overcome the employer's economic justification for the RIF. The Sixth Circuit correctly rejected this assertion, and in doing so put a dagger through the heart of the use of bald statistics of small samples in RIF cases".
The Sixth Circuit held that "statistical evidence may satisfy the fourth element in a work force reduction case... [b]ut such a small statistical sample is not probative of discrimination."

This decision highlights a recurring question regarding the use of statistics in employment litigation: what is a small sample size? Statisticians and courts alike have refused to articulate the "magic number" which, if exceeded, transforms a sample from "small" to "large". No such threshold exists. There is a statistical threshold for sample sizes below which statistical analysis is not possible (such as in multiple regression analysis, when the number of explanatory variables exceeds the number of cases being studied), but the boundary between "small" samples and "not small" samples is not discrete.

Sample size also has implications for statistical significance. Ramona Paetzhold and Steven Willborn note that "the presence of statistical significance in small sample sizes should not be discounted, however, because statistical significance is relatively difficult to obtain in low power situations" (Statistics of Discrimination, Chapter 4, p. 47). Paetzhold and Willborn continue:
Courts should not erroneously discount such results [from small sample analyses], either directly or indirectly. For example, courts should not dismiss them as unreliable or less probative because of the small sample size, nor should they substitute their own re-analysis (such as noting that small changes in the numbers would eliminate statistical significance). See, e.g., Murray v. District of Columbia.
...The only reason not to accept statistically significant results based on small samples at face value is that there is evidence that the assumptions underlying the statistical model or process that produced the test are not met... Further, it is unreasonable to assume that small sample size alone could cause the assumptions not to be met. The burden should be on the challenger to demonstrate the manner in which the assumptions of the model have been violated and that the circumstances causing the assumptions to be violated would be likely to suggest statistical significance erroneously.
Paetzhold and Willborn argue that statistically significant analyses based on "small samples" are not unreliable or less probative because of the sample size.

I agree with Mr. Hyman that Schoonmaker has the potential to require plaintiffs to provide more than pure statistics to move forward with discrimination claims in reduction in force matters with "small" sample size. However, I do not think that Schoonmaker has put a dagger through the heart of the use of statistics of small samples in RIF cases. "Small sample size" is amorphously defined and is somewhat of a moving target. It will be interesting to see if other cases accept the Schoonmaker argument, or if they will find that statistical analyses based on "small samples" do in fact have probative value.

 (Mr. Hyman's post can be found here).