FCA Litigation and Statistical Sampling

One of the current battlegrounds in cases involving large-scale fraud on the government is whether and when the government or relators may use statistical sampling to prove liability or damages. The government and relators often argue that without the ability to extrapolate results from a smaller representative sample of claims – for example, claims for reimbursement for diagnostic tests to Medicare or Medicaid – the largest frauds will go unpunished because it is impractical to try a case involving thousands of individual treatment decisions. On the other side, defendants typically object that statistical sampling and extrapolation methods deprive them of the ability to offer a defense as to each alleged false claim on an individualized basis. The resolution of this issue has dramatic consequences for many of the largest False Claims Acts cases and investigations.

Join Jeanne A. Markey, Partner and Co-Chair of the False Claims Act/Whistleblower Practice Group at Cohen Milstein Sellers & Toll, and Raymond M. Sarola, an associate in that group, as they explain and discuss the history, present state, and future implications for this evolving area of law.  

Learning Objectives: 

1. Understand statistical sampling methods and uses
2. Explore the history of statistical sampling jurisprudence for proving liability and damages, including recent cases from the Supreme Court and Court of Appeals
3. Discuss the current state of the law and potential future trends and implications for large-scale FCA cases