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TACKLING FALSE POSITIVES IN BUSINESS RESEARCH: A STATISTICAL TOOLBOX WITH APPLICATIONS
Author(s) -
Kim Jae H.
Publication year - 2019
Publication title -
journal of economic surveys
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.657
H-Index - 92
eISSN - 1467-6419
pISSN - 0950-0804
DOI - 10.1111/joes.12303
Subject(s) - toolbox , econometrics , statistical hypothesis testing , empirical research , bayesian probability , statistical inference , sample size determination , computer science , false positive paradox , range (aeronautics) , sample (material) , economics , statistics , mathematics , machine learning , artificial intelligence , engineering , chemistry , chromatography , programming language , aerospace engineering
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.