Before delving into the substance of my first post, I wanted to thank the crew at Concurring Opinions for inviting me to guest blog this month.
Recently, I have been thinking about whether empirical legal scholars have or should have special ethical responsibilities. Why special responsibilities? Two basic reasons. First, nearly all law reviews lack formal peer review. The lack of peer review potentially permits dubious data to be reported without differentiation alongside quality data. Second, empirical legal scholarship has the potential to be extremely influential on policy debates because it provides “data” to substantiate or refute claims. Unfortunately, many consumers of empirical legal scholarship — including other legal scholars, practitioners, judges, the media, and policy makers — are not sophisticated in empirical methods. Even more importantly, subsequent citations of empirical findings by legal scholars rarely take care to explain the study’s qualifications and limitations. Instead, subsequent citations often amplify the “findings” of the empirical study by over-generalizing the results.
My present concern is about weak data. By weak data, I don’t mean data that is flat out incorrect (such as from widespread coding errors) or that misuses empirical methods (such as when the model’s assumptions are not met). Others previously have discussed issues relating to incorrect data and analysis in empirical legal studies. Rather, I am referring to reporting data that encourages weak or flawed inferences, that is not statistically significant, or that is of extremely limited value and thus may be misused. The precise question I have been considering is under what circumstances one should report weak data, even with an appropriate explanation of the methodology used and its potential limitations. (A different yet related question for another discussion is whether one should report lots of data without informing the reader which data the researcher views as most relevant. This scattershot approach has many of the same concerns as weak data.)