Advantages of Random Test Plaintiff Selection
Companies are facing tort liability cases that are brought forward as class action suits with many plaintiffs. In cases involving hundreds or thousands of plaintiffs, courts are increasingly permitting discovery, and potential resolution of the claims, based on representative plaintiffs. Moreover, in toxic torts in states such as Missouri, Texas, and California, specific representative test plaintiffs are providing guidance to the courts and the litigants in evaluating all the remaining cases. It is critically important for each case that test plaintiffs are chosen that allow for a fair treatment of issues relevant to the entire group and all parties.
Regardless of the exact number of test plaintiffs the courts determine to be appropriate, the choice of who will serve as test plaintiffs ultimately should be decided by some version of random selection, which is impartial selection, and thus, fundamentally scientific. Random selection guarantees that the target population, represented by the sample, does, in fact, reflect the whole population from which the sample was drawnand not a smaller, special subgroup. Random selection guards against the dangers of making inferences about the whole population on the basis of a sample from a nonrepresentative group.
The text, Statistical Concepts and Methods, by G. K. Bhattacharyya and R. A. Johnson, illustrates the potential dangers of not randomly sampling the whole population. The authors explain why a Literary Digest poll failed to predict the winner in the national election of 1936 between presidential candidates Franklin D. Roosevelt and Alf P. Landon. According to the text, although a large-scale survey was conducted, these pollsters drew their sample from such sources as lists of telephone and car owners. In those days, such amenities were much more common among upper-income groups, and the sample consequently failed to adequately represent the low-income groups. Because support for the Republican candidate happened to be strongest in upper-income classes, the poll wrongly predicted a defeat for Roosevelt. The importance of randomization cannot be overemphasized. Random selection is the most scientifically accurate and fairest method for selecting test plaintiffs for a trial.
In a landmark case in the late 1980s, a Missouri circuit court randomly selected a small group of test plaintiffs to represent claimants who were former residents of Times Beach. The case centered on alleged adverse health effects on residents from exposure to dioxin. The judge ordered the random selection of this group, which was challenged by the plaintiffs who wanted to negotiate a selection. This challenge, however, was discouraged because there is an inherent problem associated with nonrandom sampling in this and all cases. When nonrandom sampling is used, there may be a loss of significant information to both plaintiffs and defendants. If, for example, the plaintiffs were to select those people with the greatest perceived claims, then those test plaintiffs would not provide any realistic estimate regarding the nontest plaintiffs with lesser perceived claims. Conversely, if the defendants were to select as test plaintiffs those people with the least perceived claims, then those test plaintiffs would not provide useful information about others with greater perceived claims.
One method often proposed for test cases is the selection of equal numbers of plaintiffs by the plaintiffs counsel and the defense counsel for early discovery and possibly bellwether trials. Although allowing the plaintiffs and defendants to select test plaintiffs might shed some light on the evidence and the verdicts for the extreme cases, this method would provide no useful information about the potential evidence and verdicts for the majority of the plaintiffs, whose perceived claims are in the middle. This negotiated approach is like attempting to estimate daily or annual rainfall by examining only the days with the greatest and the least rainfall. Random sampling avoids such pitfalls by providing greater information about all potential evidence and verdicts.
Clearly, when a court selects a group of plaintiffs smaller than the whole for any purpose, the preferred method is random selection, which has been the hallmark of statistical sampling for years. It avoids bias and enjoys the benefit of balancing all factors, including those that are unknown or unquantified, as well as those that are known.
Although random sampling is an easily implemented and understood sampling procedure, its significance must be explained by one well-versed in statistical concepts and their application, risk communication, and courtroom strategies. When these concepts are combined, a compelling argument about the benefits of random sampling will be conveyed to judges and juries and its acceptance gained. This background also helps the litigation team determine optimal sample sizes on a case-by-case basis and avoid pitfalls in both the selection process and the use of the results for the test plaintiffs. |