Setting Statistical Hurdles for Publishing in Accounting

Teoh Siew Hong () and Zhang Yinglei
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Teoh Siew Hong: UCLA Anderson School of Management, Los Angeles, CA, USA
Zhang Yinglei: Chinese University of Hong Kong, Hong Kong, China

Accounting, Economics, and Law: A Convivium, 2025, vol. 15, issue 1, 141-154

Abstract: Ohlson (Empirical Accounting Seminars: Elephants in the Room. Accounting, Economics, and Law: A Convivium 15 (1): 1–8) argues that researchers tacitly avoid raising statistics-related ‘elephants’ that could undermine inferences. We offer a balanced perspective, first applauding the remarkable progress made in deriving testable predictions, leveraging modern statistical techniques, and tapping alternative Big Data sources to address issues relevant to practitioners, regulators and academia. While we concur with Ohlson’s elephants, we caution against over-criticism based on statistical design choices, as it risks creating new elephants. Our key lessons: focus on meaningful hypotheses, recognize merits of descriptive studies, balance Type I and II errors in data handling and journal reviewing, employ proper context when interpreting statistical significance and consider economic significance. Overall, though empirical accounting research faces challenges, criticism should not deter innovative research (Type II error in journal reviewing).

Keywords: Fama-Macbeth regression; fixed effects; sample selection bias; Type I and Type II errors; p-hacking; statistical design choices (search for similar items in EconPapers)
JEL-codes: C12 M40 M41 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1515/ael-2022-0104

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