38 Pages Posted: 10 Jan 2017 Last revised: 17 Jul 2017
Date Written: July 17, 2017
Given the competition for top journal space, there is an incentive to produce “significant” results. With the combination of unreported tests, lack of adjustment for multiple tests, and direct and indirect p-hacking, many of the results being published will fail to hold up in the future. In addition, there are basic issues with the interpretation of statistical significance. Increasing thresholds may be necessary, but still may not be sufficient: if the effect being studied is rare, even t > 3 will produce a large number of false positives. Here I explore the meaning and limitations of a p-value. I offer a simple alternative (the minimum Bayes factor). I present guidelines for a robust, transparent research culture in financial economics. Finally, I offer some thoughts on the importance of risk taking (from the perspective of authors and editors) to advance our field.
The transcript and presentation slides are available here: http://ssrn.com/abstract=2895842.
Keywords: P-hacking, Multiple testing, Selection, Data mining, Data dredging, Rare incidence, Type I error, Type II error, P-values, Minimum Bayes Factor, MBF, SD-MBF, Bayesian P-values
JEL Classification: G00, G10, G20, G30, C10, C11, C12, C58, M41, B41
Suggested Citation: Suggested Citation
Harvey, Campbell R., The Scientific Outlook in Financial Economics (July 17, 2017). Duke I&E Research Paper No. 2017-05. Available at SSRN: https://ssrn.com/abstract=2893930 or http://dx.doi.org/10.2139/ssrn.2893930