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Significance Testing in Accounting Research: A Critical Evaluation Based on Evidence

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  • Jae H. Kim
  • Kamran Ahmed
  • Philip Inyeob Ji

Abstract

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.

Suggested Citation

  • Jae H. Kim & Kamran Ahmed & Philip Inyeob Ji, 2018. "Significance Testing in Accounting Research: A Critical Evaluation Based on Evidence," Abacus, Accounting Foundation, University of Sydney, vol. 54(4), pages 524-546, December.
    • Handle: RePEc:bla:abacus:v:54:y:2018:i:4:p:524-546
    DOI: 10.1111/abac.12141
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    Cited by:

    1. Jae H. Kim & In Choi, 2021. "Choosing the Level of Significance: A Decision‐theoretic Approach," Abacus, Accounting Foundation, University of Sydney, vol. 57(1), pages 27-71, March.
    2. Johnstone, David, 2022. "Accounting research and the significance test crisis," CRITICAL PERSPECTIVES ON ACCOUNTING, Elsevier, vol. 89(C).
    3. Xi Fu & Xiaoxi Wu & Zhifang Zhang, 2021. "The Information Role of Earnings Conference Call Tone: Evidence from Stock Price Crash Risk," Journal of Business Ethics, Springer, vol. 173(3), pages 643-660, October.
    4. James A. Ohlson, 2022. "Researchers’ data analysis choices: an excess of false positives?," Review of Accounting Studies, Springer, vol. 27(2), pages 649-667, June.
    5. Kim, Jae H. & Shamsuddin, Abul, 2023. "Stock market anomalies: An extreme bounds analysis," International Review of Financial Analysis, Elsevier, vol. 90(C).
    6. Thomas R. Dyckman & Stephen A. Zeff, 2019. "Important Issues in Statistical Testing and Recommended Improvements in Accounting Research," Econometrics, MDPI, vol. 7(2), pages 1-11, May.
    7. Hok-Ko Pong & Chun-Cheong Fong, 2024. "The Impact of Personal Satisfaction on the Environmental, Social, and Governance Practices of Chinese Accounting Managers," Sustainability, MDPI, vol. 16(14), pages 1-26, July.
    8. Jae H. Kim & Andrew P. Robinson, 2019. "Interval-Based Hypothesis Testing and Its Applications to Economics and Finance," Econometrics, MDPI, vol. 7(2), pages 1-22, May.
    9. Muhammad Ishaq Bhatti & Jae H. Kim, 2020. "Towards a New Paradigm for Statistical Evidence in the Use of p -Value," Econometrics, MDPI, vol. 9(1), pages 1-3, December.
    10. Dan Hu & Eunju Lee & Bingxin Li, 2023. "Trade secrets protection and stock price crash risk," The Financial Review, Eastern Finance Association, vol. 58(2), pages 395-421, May.
    11. Stewart Jones & Nurul Alam, 2019. "A machine learning analysis of citation impact among selected Pacific Basin journals," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 59(4), pages 2509-2552, December.
    12. Jae H. Kim & Philip I. Ji, 2024. "Testing for signal-to-noise ratio in linear regression: a test under large or massive sample," Review of Managerial Science, Springer, vol. 18(10), pages 3007-3024, October.
    13. Chen, Xiaomeng Charlene & Jones, Stewart & Hasan, Mostafa Monzur & Zhao, Ruoyun & Alam, Nurul, 2023. "Does strategic deviation influence firms’ use of supplier finance?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 85(C).
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    15. Kellner, Ralf & Rösch, Daniel, 2021. "A Bayesian Re-Interpretation of “significant” empirical financial research," Finance Research Letters, Elsevier, vol. 38(C).

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