Size matters: the standard error of regressions in the American Economic Review

• Published in: The Journal of socio-economics Vol. 33; no. 5; pp. 527 - 546
• Main Authors: Ziliak, Stephen T., McCloskey, Deirdre N.
• Format: Journal Article
• Language: English
• Published: Greenwich Elsevier Inc 01-11-2004; Elsevier; Elsevier Science Ltd
• Series: The Journal of Socio-Economics
• Subjects: American Economic Review; Econometric Analysis; Economic models; Economic theory; Errors; Hypotheses; Methodological Problems; Random number sampling; Regression; Regression Analysis; Significance; Size; Social sciences; Standard error; Statistical Bias; Statistical methods; Statistical Significance; Studies; Testing; C10; B23; C12; American Economic Review; Regression; Standard error; Significance; A20; Testing
• ISSN: 1053-5357; 2214-8043; 1879-1239; 2214-8051
• DOI: 10.1016/j.socec.2004.09.024

Significance testing as used has no theoretical justification. Our article in the
Journal of Economic Literature (1996) showed that of the 182 full-length papers published in the 1980s in the
American Economic and Review 70% did not distinguish economic from statistical significance. Since 1996 many colleagues have told us that practice has improved. We interpret their response as an empirical claim, a judgment about a fact. Our colleagues, unhappily, are mistaken: significance testing is getting worse. We find here that in the next decade, the 1990s, of the 137 papers using a test of statistical significance in the
AER fully 82% mistook a merely statistically significant finding for an economically significant finding. A super majority (81%) believed that looking at the sign of a coefficient sufficed for science, ignoring size. The mistake is causing economic damage: losses of jobs and justice, and indeed of human lives (especially in, to mention another field enchanted with statistical significance as against substantive significance, medical science). The confusion between fit and importance is causing false hypotheses to be accepted and true hypotheses to be rejected. We propose a publication standard for the future: “Tell me the oomph of your coefficient; and do not confuse it with merely statistical significance.”