TACKLING FALSE POSITIVES IN BUSINESS RESEARCH: A STATISTICAL TOOLBOX WITH APPLICATIONS
针对商业实证研究中假阳性发现泛滥的问题，提出一个包含贝叶斯方法、最优显著性水平等工具的统计工具箱，并应用于三项金融研究，发现基于p值标准的结果不成立。
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.