Tackling False Positives in Business Research: A Statistical Toolbox with Applications

34 Pages Posted: 28 May 2020

See all articles by Jae H. Kim

Jae H. Kim

affiliation not provided to SSRN

Date Written: July 2019

Abstract

Serious concerns have been raised that false positive findings are widespread in empirical research in business disciplines. 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 a range of alternatives to the p‐value criterion such as the Bayesian methods, optimal significance level, sample size selection, equivalence testing and exploratory data analyses. 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: Adaptive significance level, Bayes factor, Decision theory, Fallacy of rejection, Large‐sample bias, Zero probability paradox

JEL Classification: C12, G10

Suggested Citation

Kim, Jae H., Tackling False Positives in Business Research: A Statistical Toolbox with Applications (July 2019). Journal of Economic Surveys, Vol. 33, Issue 3, pp. 862-895, 2019, Available at SSRN: https://ssrn.com/abstract=3608909 or http://dx.doi.org/10.1111/joes.12303

Jae H. Kim (Contact Author)

affiliation not provided to SSRN

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