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Significance Testing in Accounting Research: A Critical Evaluation Based on Evidence
Author(s) -
Kim Jae H.,
Ahmed Kamran,
Ji Philip Inyeob
Publication year - 2018
Publication title -
abacus
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.632
H-Index - 45
eISSN - 1467-6281
pISSN - 0001-3072
DOI - 10.1111/abac.12141
Subject(s) - null hypothesis , statistical significance , statistical hypothesis testing , significance testing , statistical power , statistical inference , p value , sample (material) , type i and type ii errors , accounting , sample size determination , econometrics , empirical research , inference , statistics , economics , mathematics , computer science , artificial intelligence , chemistry , chromatography
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.