Tackling False Positives in Finance: A Statistical Toolbox With Applications
48 Pages Posted: 24 Jun 2018 Last revised: 22 Jul 2018
Date Written: June 7, 2018
Serious concerns have been raised that false positive findings are widespread in empirical research in finance. This is largely because researchers almost exclusively adopt the "p-value less than 0.05" criterion for statistical significance; and they are often not fully aware of large-sample biases which can potentially mislead their research outcomes. This paper proposes that a statistical toolbox (rather than a single hammer) be used in empirical research, which offers researchers a range of statistical instruments, including alternatives to the p-value criterion and cautionary analyses for large-sample bias. It is found that the positive results obtained under the p-value criterion cannot stand, when the toolbox is applied to three notable studies in finance.
Keywords: Optimal Significance Level, Bayes Factor, Decision Theory, Fallacy of Rejection, Large-Sample Bias
JEL Classification: C12, G10
Suggested Citation: Suggested Citation