Statistical Significance in the New Tom and the Old Tom: A Reply to Thomas Mayer
Econometricians have been claiming proudly since World War II that significance testing is the empirical side of economics. In fact today most young economists think that the word “empirical” simply means “collect enough data to do a significance test”. Tjalling Koopmans’s influential book of 1957, Three Essays on the State of Economic Science, solidified the claim. A century of evidence after Student’s t-test points strongly to the opposite conclusion. Against conventional econometrics we argue that statistical significance is neither necessary nor sufficient for proving commercial, human, or scientific importance. A recent comment by Thomas Mayer, though in parts insightful, does nothing to alter conclusions about the logic and evidence which we and others have assembled against significance testing. Let’s bury it, and get on to empirical work that actually changes minds.
Volume (Year): 9 (2012)
Issue (Month): 3 (September)
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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Mayer, Thomas, 1980. "Economics as a Hard Science: Realistic Goal or Wishful Thinking?," Economic Inquiry, Western Economic Association International, vol. 18(2), pages 165-78, April.
- Kevin Hoover & Mark Siegler, 2008. "The rhetoric of 'Signifying nothing': a rejoinder to Ziliak and McCloskey," Journal of Economic Methodology, Taylor & Francis Journals, vol. 15(1), pages 57-68.
- Kevin D. Hoover & Mark V. Siegler, 2005.
"Sound and Fury: McCloskey and Significance Testing in Economics,"
- Kevin Hoover & Mark Siegler, 2008. "Sound and fury: McCloskey and significance testing in economics," Journal of Economic Methodology, Taylor & Francis Journals, vol. 15(1), pages 1-37.
- Soyer, Emre & Hogarth, Robin M., 2012. "The illusion of predictability: How regression statistics mislead experts," International Journal of Forecasting, Elsevier, vol. 28(3), pages 695-711.
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