Empirical Legal Studies

JELS 10:3

As a co-editor I note with pride that JELS 10:3 maintains (for more than one decade) JELS' perfect record of on-time publication and includes a wonderful collection of diverse and interesting papers. Topics in this issue range from data on the Indian Supreme Court's workload (here) to multidistrict litigation transfers and consolidations (here).

History of the p < 0.05 Significance Level

Prompted by my prior post discussing statistical significance levels, my Cornell colleague Ted Eisenberg passed along this 1982 paper from the American Psychologist by Michael Cowles (York) and Caroline Davis (York) discussing the emergence of the p < 0.05 threshold as the "standard" in the social sciences. Cowles and Davis argue that the move to the p < 0.05 threshold pre-dates Sir Ronald Fisher's contribution.

Statistical Significance Thresholds

Rafael Irizarry makes an interesting contribution to a discussion about whether the standard statistical significance threshold (p < 0.05) should be tightened up.

"The gist of my thought is that, for some scientific fields, the pessimist's criticism is missing a critical point: that in practice, there is an inverse relationship between increasing rates of true discoveries and decreasing rates of false discoveries and that true discoveries from fields such as the biomedical sciences provide an enormous benefit to society."

While Irizarry's comment dwells on the hard sciences, recognizing the inevitable trade off between stringency and discovery (by plotting true positive and false positive rates for a given classifying procedure) potentially implicates empirical legal scholarship as well.

OLS Regression Assumptions

From the archives comes a helpful reminder from Osborne & Waters about key regression assumptions that require examination (here). Gelman provides a "friendly" amendment and a (related) list of his own.

More on Statistical Power

To prolong my recent mini-obsession on statistical power issues, I thought some readers might find a three-part statistical power "webinar" helpful. Hosted by UCLA's outstanding Institute for Digital Research and Education (idre), the power analysis series includes introductory, intermediate, and advanced instruction. Insofar as power issues are too often ignored by researchers, this resource is worth a look.