Statistical Analysis Requires an Expert Witness with Statistical Expertise

Christina K. Connearne sued her employer, Main Line Hospitals, for age discrimination. Main Line charged Connearne with fabricating medical records, but Connearne replied that the charge was merely a pretext. Connearney v. Main Line Hospitals, Inc., Civ. Action No. 15-02730, 2016 WL 6569292 (E.D. Pa. Nov. 4, 2016) [cited as Connearney]. Connearne’s legal counsel engaged Christopher Wright, an expert witness on “human resources,” for a variety of opinions, most of which were not relevant to the action. Alas, for Ms. Connearne, the few relevant opinions proffered by Wright were unreliable. On a Rule 702 motion, Judge Pappert excluded Wright from testifying at trial.

Although not a statistician, Wright sought to offer his statistical analysis in support of the age discrimination claim. Connearney at *4. According to Judge Pappert’s opinion, Wright had taken just two classes in statistics, but perhaps His Honor meant two courses. (Wright Dep., at 10:3–4.) If the latter, then Wright had more statistical training than most physicians who are often permitted to give bogus statistical opinions in health effects litigation. In 2015, the Medical College Admission Test apparently started to include some very basic questions on statistical concepts. Some medical schools now require an undergraduate course in statistics. See Harvard Medical School Requirements for Admission (2016). Most medical schools, however, still do not require statistical training for their entering students. See Veritas Prep, “How to Select Undergraduate Premed Coursework” (Dec. 5, 2011); “Georgetown College Course Requirements for Medical School” (2016).

Regardless of formal training, or lack thereof, Christopher Wright demonstrated a profound ignorance of, and disregard for, statistical concepts. (Wright Dep., at 10:15–12:10; 28:6–14.) Wright was shown to be the wrong expert witness for the job by his inability to define statistical significance. When asked what he understood to be a “statistically significant sample,” Wright gave a meaningless, incoherent answer:

I think it depends on the environment that you’re analyzing. If you look at things like political polls, you and I wouldn’t necessarily say that serving [sic] 1 percent of a population is a statistically significant sample, yet it is the methodology that’s used in the political polls. In the HR field, you tend to not limit yourself to statistical sampling because you then would miss outliers. So, most HR statistical work tends to be let’s look at the entire population of whatever it is we’re looking at and go from there.”

Connearney at *5 (Wright Dep., at 10:15–11:7). When questioned again, more specifically on the meaning of statistical significance, Wright demonstrated his complete ignorance of the subject:

Q: And do you recall the testimony it’s generally around 85 to 90 employees at any given time, the ER [emergency room]?

A: I don’t recall that specific number, no.

Q: And four employees out of 85 or 90 is about what, 5 or 6 percent?

A: I’m agreeing with your math, yes.

Q: Is that a statistically significant sample?

A: In the HR [human resources] field it sure is, yes.

Q: Based on what?

A: Well, if one employee had been hit, physically struck, by their boss, that’s less than 5 percent. That’s statistically significant.”

Connearney at *5 n.5 (Wright Dep., at 28:6–14)

In support of his opinion about “disparate treatment,” Wright’s report contained nothing than a naked comparison of two raw percentages and a causal conclusion, without any statistical analysis. Even for this simplistic comparison of rates, Wright failed to explain how he obtained the percentages in a way that permitted the parties and the trial court to understand his computation and his comparisons. Without a statistical analysis, the trial court concluded that Wright had failed to show that the disparity in termination rates among younger and older employees was not likely consistent with random chance. See also Moultrie v. Martin, 690 F. 2d 1078 (4th Cir. 1982) (rejecting writ of habeas corpus when petitioner failed to support claim of grand jury race discrimination with anything other than the numbers of white and black grand jurors).

Although Wright gave the wrong definition of statistical significance, the trial court relied upon judges of the Third Circuit who also did not get the definition quite right. The trial court cited a 2010 case in the Circuit, which conflated substantive and statistical significance and then gave a questionable definition of statistical significance:

The Supreme Court has not provided any definitive guidance about when statistical evidence is sufficiently substantial, but a leading treatise notes that ‘[t]he most widely used means of showing that an observed disparity in outcomes is sufficiently substantial to satisfy the plaintiff’s burden of proving adverse impact is to show that the disparity is sufficiently large that it is highly unlikely to have occurred at random.’ This is typically done by the use of tests of statistical significance, which determine the probability of the observed disparity obtaining by chance.”

See Connearney at *6 & n.7, citing and quoting from Stagi v. National RR Passenger Corp., 391 Fed. Appx. 133, 137 (3d Cir. 2010) (emphasis added) (internal citation omitted). Ultimately, however, this was all harmless error on the way to the right result.

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