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Statistical Evidence in Wage and Hour Class Actions: Impact on Certification and Trial

Disputing or Leveraging Representative Sampling to Prove Classwide Liability and Damages

Recording of a 90-minute CLE video webinar with Q&A

This program is included with the Strafford CLE Pass. Click for more information.
This program is included with the Strafford All-Access Pass. Click for more information.

Conducted on Thursday, June 15, 2023

Recorded event now available

or call 1-800-926-7926

This CLE course will arm employment counsel with the latest strategic approaches for disputing or leveraging the use of statistical evidence to establish classwide liability in wage and hour actions. The panel will discuss recent decisions discussing the use of statistical evidence, review statistical sampling techniques relevant to wage and hour cases, and examine best practices for employment counsel going forward.


The use of statistical evidence has long been part of complex wage and hour litigation and has evolved since the U.S. Supreme Court ruled against the employer in Tyson Foods Inc. v. Bouaphakeo, holding that statistical sampling was a permissible form of evidence that a jury may consider in determining hours employees worked and to establish classwide liability in an FLSA action.

In some cases, plaintiffs seek to use the testimony of a representative sample of employees to establish a prima facie case under the FLSA or state wage and hour statutes. As such, defense counsel may challenge the propriety of using statistical sampling and the soundness of any statistical methodology.

Plaintiffs may also attempt to use statistical sampling when the universe of data is not available, or no data is available, to establish liability and class-wide damages. When limited data is available (a sample), plaintiffs may apply inferential statistical analysis to prove/disprove classwide assertions provided the sample meets the criteria of being representative of the population. If no data is available, plaintiffs can utilize a survey of the class members to establish class-wide liability and damages.

Effective defense techniques include contesting whether the use of sampling would be appropriate under the framework established in Tyson Foods, challenging whether the sample used is representative, questioning surveys and their methodology, and arguing that a more individualized determination of liability and damages is required by the Rules Enabling Act or due process.

Listen as our distinguished panel reviews strategic approaches to disputing or leveraging statistical evidence in wage and hour class actions in light of Tyson Foods and its progeny. Our panel will discuss the uses of representative evidence and statistical sampling techniques relevant to wage and hour cases and offer best practices for plaintiff and defense counsel.



  1. Representative evidence and liability
  2. Plaintiff strategies for leveraging statistical evidence
  3. Defense strategies for attacking statistical evidence


The panel will review these and other key issues:

  • How to meet the Daubert standard to have the statistical analysis admitted under Rule 702
  • What types of statistics can be introduced during certification and trial, and what are the proper ways of utilization?
  • What are the most effective challenges to the use of statistical sampling?
  • Under what circumstances should counsel advocate for alternative ways to prove damages as opposed to extrapolating from samples?


Barnes, Kevin
Kevin T. Barnes

Law Offices of Kevin T. Barnes

Mr. Barnes is one of the first attorneys to litigate wage and hour cases in California beginning in 1997 and has tried...  |  Read More

Forsey, Morgan
Morgan Forsey

Sheppard Mullin

Ms. Forsey handles all facets of the litigation and trial process in labor and employment disputes. She has significant...  |  Read More

Petersen, Jeffrey
Jeffrey S. Petersen, Ph.D.

Allman & Petersen Economics

Mr. Petersen, Ph.D. has served as a statistical and damages expert for plaintiffs and defendants in over 70 class...  |  Read More

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