The Department of Justice recovered more than $3.7B in 2017 in settlements and judgments from civil cases brought under the False Claims Act (FCA). Overall recoveries since 1986, when Congress substantially strengthened the FCA, now amount to more than $56B. As these recoveries make clear, the FCA is a vital tool in the arsenal both of the Government and lawyers for whistleblowers to attack what they allege to be fraud on the Government. As plaintiffs, they continue to bring sophisticated claims involving, for instance, Medicare and Medicaid and government contracts, and thus have turned to statistical sampling as a way to help them prove these large-scale cases. As plaintiffs, they routinely argue that the use of statistical evidence to prove liability under the statute should be allowed to avoid difficult and prolonged litigation. By contrast, defendants in these actions have asserted that the FCA requires claim-by-claim analysis and that sampling is an impermissible shortcut to establishing liability.
The debate over sampling in FCA cases continues to rage. Several recent court decisions have tackled the issue of sampling directly and have reached different conclusions. Thus, the battle over statistical evidence—how to harness its possibilities and how to defend against them—will continue. This program will consider the history and use of statistical sampling both in FCA cases and in other civil contexts to understand the many opportunities and challenges presented by this evidence to the future of FCA litigation.
Key topics to be discussed:
- Statistical Sampling Theories, Methodologies, and Historic Uses in Civil Cases
- Current Trends in Statistical Sampling in FCA Cases
- Recent Court Decisions
- Best Practices, Including Daubert Issues
