Statistical Non-Significance in Empirical Economics
Significance tests are probably the most common form of inference in empirical economics, and significance is often interpreted as providing greater informational content than non-significance. In this article we show, however, that rejection of a point null often carries very little information, while failure to reject may be highly informative. This is particularly true in empirical contexts that are typical and even prevalent in economics, where data sets are large (and becoming larger) and where there are rarely reasons to put substantial prior probability on a point null. Our results challenge the usual practice of conferring point null rejections a higher level of scientific significance than non-rejections. In consequence, we advocate a visible reporting and discussion of non-significant results in empirical practice.
I thank Isaiah Andrews, Joshua Angrist, Amy Finkelstein, Guido Imbens, Ben Olken, and especially Gary Chamberlain and Max Kasy for comments and discussions. The views expressed herein are those of the author and do not necessarily reflect the views of the National Bureau of Economic Research.
Alberto Abadie, 2020. "Statistical Nonsignificance in Empirical Economics," American Economic Review: Insights, American Economic Association, vol. 2(2), pages 193-208, June. citation courtesy of