Significance Testing in Accounting Research: A Critical Evaluation Based on Evidence
调查2014年顶级会计期刊论文发现，研究者几乎只用常规显著性水平，忽视样本量和检验功效，导致统计推断偏向第一类错误，建议改革当前显著性检验实践以提高实证研究的可信度。
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.