Statistical Arguments, SCOTUS, Tyson, and Be Careful What You Wish For
Excluding Justices Thomas and Alito, SCOTUS this week not surprisingly concluded that statistical proof can be used in class actions to prove up damages in at least some cases, even if there are some differences between class members. As presented by SCOTUSblog, the issues in Tyson were:
“Issue: (1) Whether differences among individual class members may be ignored and a class action certified under Federal Rule of Civil Procedure 23(b)(3), or a collective action certified under the Fair Labor Standards Act, where liability and damages will be determined with statistical techniques that presume all class members are identical to the average observed in a sample; and (2) whether a class action may be certified or maintained under Rule 23(b)(3), or a collective action certified or maintained under the Fair Labor Standards Act, when the class contains hundreds of members who were not injured and have no legal right to any damages.”
This Tyson Foods outcome should not surprise anyone who thought beyond the narrow bounds of partisan thinking. After all, clinical trials for lifesaving drugs, for example, often succeed or fail based on statistics, even though it is well known that the people in the trial are individually variable humans.
The Tyson case also presented an element of “be careful what you wish for.” Imagine, for example, the plight of defendant companies if they were barred from using statistical proof (i.e. epidemiology) to defeat claims with a relative risk of below 2.0 (to date, most such calculations defy reality by assuming all humans are the same, which we know is not true).