Random Sample Size Needs to Be One Million Reports, Court Says: eDiscovery Case Law

It’s a three-post day! In Healy v. Milliman, Inc., No. C20-1473-JCC (W.D. Wash. Nov. 12, 2021), Washington District Judge John C. Coughenor granted in part and denied in part the plaintiff’s motion to compel the production of documents responsive to a specific Request for Production (“RFP”), finding that the information requested was responsive, but unduly burdensome and not proportional to the plaintiff’s pre-certification needs. So, Judge Coughenor endorsed the sampling approach suggested by the defendant in its supplemental briefing but ordered a random sample size of one million documents instead of the 100,000 that the defendant proposed.

Case Background

In this Fair Credit Reporting Act (“FCRA”) class-action case over inaccurate reporting and failure to adequately investigate errors regarding the named plaintiff’s health history which caused him to be denied insurance coverage, the plaintiff sought to compel the production of documents responsive to RFP No. 66, which sought “all IRIX data for applicants during the class period which fall within the group of 13% of data matches that are not matched based on the applicant’s social security number.”

Among other objections, the defendant asserted that the request was unduly burdensome, explaining that, based on how the defendant stores its data, it would need to sort through 43 million reports prepared during the relevant period, rather than just the reports that Plaintiff seeks discovery on. The defendant also explained that the 13% of records sought by the plaintiff refer only to matches for a single data source and, absent sampling, a fully responsive production to RFP No. 66 would actually entail the production of at least 38 million reports.

Judge’s Ruling

Judge Coughenor stated: “Parties may obtain discovery regarding any nonprivileged matter that is (1) relevant to any party’s claim or defense, and (2) proportional to the needs of the case, considering the Rule 26(b)(1) factors…Discovery, however, ‘has ultimate and necessary boundaries.’…Accordingly, the Court must limit discovery if it determines that the burden or expense of the proposed discovery outweighs its likely benefit, considering the needs of the case, the amount in controversy, the parties’ resources, the importance of the issues at stake in the action, and the importance of the discovery in resolving the issues.”

Continuing, he said: “The Court agrees that production responsive to RFP No. 66 is necessary to provide Plaintiff the discovery it needs to support its class allegations…However, the production sought is unduly burdensome and not proportional to Plaintiff’s pre-certification needs. The Court, therefore, endorses the sampling approach suggested by Defendant in its supplemental briefing but finds that a larger sample size is required.”

As a result, Judge Coughenor granted in part and denied in part the plaintiff’s motion, ordering the defendant to “produce the requested data for the 23 fields identified by Plaintiff for one-million randomly selected reports {instead of the 100,000 report random sample size the defendant suggested}, without reference to any particular match type, i.e., not limited to those reports lacking social security number and/or birth date matching. Relatedly, Defendant must also produce the methodology it uses to select the sampled report data, to ensure the selection is, in fact, truly random.”

So, what do you think?  Is a one million report random sample size necessary to gather the information needed or could a smaller random sample size work just as well?  Please share any comments you might have or if you’d like to know more about a particular topic.

Case opinion link courtesy of eDiscovery Assistant, an Affinity partner of eDiscovery Today.

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4 comments

  1. Sigh. It’s time for eDiscovery folx to start understanding that the appropriate sample size is not based on magic; it depends on the prevalence of what it is you are looking for! The rarer the thing you are looking for, the larger the sample size needs to be in order to be able to say anything meaningful based on your sample. That’s why when you are looking for a responsive needle in a non-responsive haystack (e.g., using an elusion sample), a small sample tells you practically nothing when you find nothing responsive there.

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