Significance Testing in Accounting Research: A Critical Evaluation Based on Evidence
Jae Kim (),
Kamran Ahmed and
Philip Inyeob Ji
Abacus, 2018, vol. 54, issue 4, 524-546
From a survey of the papers published in leading accounting journals in 2014, we find that accounting researchers conduct significance testing almost exclusively at a conventional level of significance, without considering key factors such as the sample size or power of a test. We present evidence that a vast majority of the accounting studies favour large or massive sample sizes and conduct significance tests with the power extremely close to or equal to one. As a result, statistical inference is severely biased towards Type I error, frequently rejecting the true null hypotheses. Under the ‘p‐value less than 0.05’ criterion for statistical significance, more than 90% of the surveyed papers report statistical significance. However, under alternative criteria, only 40% of the results are statistically significant. We propose that substantial changes be made to the current practice of significance testing for more credible empirical research in accounting.
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