Size matters: the standard error of regressions in the American Economic Review
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...
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| Published in: | The Journal of socio-economics Vol. 33; no. 5; pp. 527 - 546 |
|---|---|
| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Greenwich
Elsevier Inc
01-11-2004
Elsevier Elsevier Science Ltd |
| Series: | The Journal of Socio-Economics |
| Subjects: | |
| Online Access: | Get full text |
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| Abstract | 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.” |
|---|---|
| AbstractList | Significance testing as used has no theoretical justification. Our article in the Journal of Economic Literature (1996) showed that of the 192 full-length papers published in the 1980s in the American Economic 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 & justice, & indeed of human lives (especially in, to mention another field enchanted with statistical significance as against substantive significance, medical science). The confusion between fit & importance is causing false hypotheses to be accepted & true hypotheses to be rejected. We propose a publication standard for the future: "Tell me the oomph of your coefficient; & do not confuse it with merely statistical significance.". 5 Tables, 49 References. [Copyright 2004 Elsevier Inc.] 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.” 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." [PUBLICATION ABSTRACT] |
| Author | Ziliak, Stephen T. McCloskey, Deirdre N. |
| Author_xml | – sequence: 1 givenname: Stephen T. surname: Ziliak fullname: Ziliak, Stephen T. email: sziliak@roosevelt.edu organization: School of Policy Studies, College of Arts and Sciences, Roosevelt University, 430 S. Michigan Avenue, Chicago, IL 60605, USA – sequence: 2 givenname: Deirdre N. surname: McCloskey fullname: McCloskey, Deirdre N. email: deirdre2@uic.edu organization: College of Arts and Sciences (m/c 198), University of Illinosis at Chicago, 601 South Morgan, Chicago, IL 60607-7104, USA |
| BackLink | http://econpapers.repec.org/article/eeesoceco/v_3a33_3ay_3a2004_3ai_3a5_3ap_3a527-546.htm$$DView record in RePEc |
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| Cites_doi | 10.1037/h0044139 10.1037/h0074554 10.1038/scientificamerican0495-32 10.17763/haer.48.3.t490261645281841 10.1016/0304-4076(94)01629-E 10.1257/jep.14.1.121 10.1111/1467-9485.00056 10.1080/0893569052000312890 10.3102/00346543040003371 10.2307/2093201 |
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Journal of Economic Literature (1996) showed that of the 182 full-length... Significance testing as used has no theoretical justification. Our article in the Journal of Economic Literature (1996) showed that of the 182 full-length... Significance testing as used has no theoretical justification. Our article in the Journal of Economic Literature (1996) showed that of the 192 full-length... |
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