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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.
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