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Significance Testing: We Can Do Better

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  • Thomas R. Dyckman

Abstract

type="main"> This paper advocates abandoning null hypothesis statistical tests (NHST) in favour of reporting confidence intervals. The case against NHST, which has been made repeatedly in multiple disciplines and is growing in awareness and acceptance, is introduced and discussed. Accounting as an empirical research discipline appears to be the last of the research communities to face up to the inherent problems of significance test use and abuse. The paper encourages adoption of a meta-analysis approach which allows for the inclusion of replication studies in the assessment of evidence. This approach requires abandoning the typical NHST process and its reliance on p-values. However, given that NHST has deep roots and wide ‘social acceptance’ in the empirical testing community, modifications to NHST are suggested so as to partly counter the weakness of this statistical testing method.

Suggested Citation

  • Thomas R. Dyckman, 2016. "Significance Testing: We Can Do Better," Abacus, Accounting Foundation, University of Sydney, vol. 52(2), pages 319-342, June.
    • Handle: RePEc:bla:abacus:v:52:y:2016:i:2:p:319-342
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    File URL: http://hdl.handle.net/10.1111/abac.12078
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    Cited by:

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    6. 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.
    7. Jones, Stewart & Wang, Tim, 2019. "Predicting private company failure: A multi-class analysis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 61(C), pages 161-188.
    8. 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.
    9. 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.
    10. 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.

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