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Moving to a world beyond p-value

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  • Jae H. Kim

    (La Trobe University)

Abstract

In light of the recent statements on the p-value criterion made by the American Statistical Association (ASA), it is now clear that that the current paradigm of statistical significance and its decision rule should be modified, in many fields of science including the business disciplines. We need a new or modified paradigm for “thoughtful, open and modest” research. This paper explains why we should adopt the ASA recommendations, and proposes a range of possible alternatives that may be included in a new paradigm for statistical research. As an application, the effect of seasonal affective disorder on stock return is re-evaluated.

Suggested Citation

  • Jae H. Kim, 2022. "Moving to a world beyond p-value," Review of Managerial Science, Springer, vol. 16(8), pages 2467-2493, November.
    • Handle: RePEc:spr:rvmgts:v:16:y:2022:i:8:d:10.1007_s11846-021-00504-6
    DOI: 10.1007/s11846-021-00504-6
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    References listed on IDEAS

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    1. Sander Greenland, 2019. "Valid P-Values Behave Exactly as They Should: Some Misleading Criticisms of P-Values and Their Resolution With S-Values," The American Statistician, Taylor & Francis Journals, vol. 73(S1), pages 106-114, March.
    2. Alberto Abadie, 2020. "Statistical Nonsignificance in Empirical Economics," American Economic Review: Insights, American Economic Association, vol. 2(2), pages 193-208, June.
    3. Daniel J. Benjamin & James O. Berger, 2019. "Three Recommendations for Improving the Use of p-Values," The American Statistician, Taylor & Francis Journals, vol. 73(S1), pages 186-191, March.
    4. Andres Algaba & David Ardia & Keven Bluteau & Samuel Borms & Kris Boudt, 2020. "Econometrics Meets Sentiment: An Overview Of Methodology And Applications," Journal of Economic Surveys, Wiley Blackwell, vol. 34(3), pages 512-547, July.
    5. Garret Christensen & Edward Miguel, 2018. "Transparency, Reproducibility, and the Credibility of Economics Research," Journal of Economic Literature, American Economic Association, vol. 56(3), pages 920-980, September.
    6. Startz, Richard, 2014. "Choosing the More Likely Hypothesis," Foundations and Trends(R) in Econometrics, now publishers, vol. 7(2), pages 119-189, November.
    7. McCloskey, Donald N, 1985. "The Loss Function Has Been Mislaid: The Rhetoric of Significance Tests," American Economic Review, American Economic Association, vol. 75(2), pages 201-205, May.
    8. Jae H. Kim, 2020. "Decision-Theoretic Hypothesis Testing: A Primer With R Package OptSig," The American Statistician, Taylor & Francis Journals, vol. 74(4), pages 370-379, October.
    9. Guido W. Imbens, 2021. "Statistical Significance, p-Values, and the Reporting of Uncertainty," Journal of Economic Perspectives, American Economic Association, vol. 35(3), pages 157-174, Summer.
    10. Keuzenkamp, Hugo A. & Magnus, Jan R., 1995. "On tests and significance in econometrics," Journal of Econometrics, Elsevier, vol. 67(1), pages 5-24, May.
    11. Campbell R. Harvey, 2017. "Presidential Address: The Scientific Outlook in Financial Economics," Journal of Finance, American Finance Association, vol. 72(4), pages 1399-1440, August.
    12. Andrew A. Anderson, 2019. "Assessing Statistical Results: Magnitude, Precision, and Model Uncertainty," The American Statistician, Taylor & Francis Journals, vol. 73(S1), pages 118-121, March.
    13. Keim, Donald B. & Madhavan, Ananth, 1997. "Transactions costs and investment style: an inter-exchange analysis of institutional equity trades," Journal of Financial Economics, Elsevier, vol. 46(3), pages 265-292, December.
    14. Gandomi, Amir & Haider, Murtaza, 2015. "Beyond the hype: Big data concepts, methods, and analytics," International Journal of Information Management, Elsevier, vol. 35(2), pages 137-144.
    15. Mark J. Kamstra & Lisa A. Kramer & Maurice D. Levi, 2003. "Winter Blues: A SAD Stock Market Cycle," American Economic Review, American Economic Association, vol. 93(1), pages 324-343, March.
    16. De Long, J Bradford & Lang, Kevin, 1992. "Are All Economic Hypotheses False?," Journal of Political Economy, University of Chicago Press, vol. 100(6), pages 1257-1272, December.
    17. Rebecca A. Betensky, 2019. "The p-Value Requires Context, Not a Threshold," The American Statistician, Taylor & Francis Journals, vol. 73(S1), pages 115-117, March.
    18. Aris Spanos, 2018. "Mis†Specification Testing In Retrospect," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 541-577, April.
    19. Edward E. Leamer, 1988. "3 Things That Bother Me," The Economic Record, The Economic Society of Australia, vol. 64(4), pages 331-335, December.
    20. 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.
    21. Valentin Amrhein & Sander Greenland & Blake McShane, 2019. "Scientists rise up against statistical significance," Nature, Nature, vol. 567(7748), pages 305-307, March.
    22. Deirdre N. McCloskey & Stephen T. Ziliak, 1996. "The Standard Error of Regressions," Journal of Economic Literature, American Economic Association, vol. 34(1), pages 97-114, March.
    23. Schneider, Jesper W., 2013. "Caveats for using statistical significance tests in research assessments," Journal of Informetrics, Elsevier, vol. 7(1), pages 50-62.
    24. 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.
    25. David Trafimow, 2019. "A Frequentist Alternative to Significance Testing, p -Values, and Confidence Intervals," Econometrics, MDPI, vol. 7(2), pages 1-14, June.
    26. Kim, Jae H. & Ji, Philip Inyeob, 2015. "Significance testing in empirical finance: A critical review and assessment," Journal of Empirical Finance, Elsevier, vol. 34(C), pages 1-14.
    27. Jae H. Kim & In Choi, 2017. "Unit Roots in Economic and Financial Time Series: A Re-Evaluation at the Decision-Based Significance Levels," Econometrics, MDPI, vol. 5(3), pages 1-23, September.
    28. James A. Ohlson, 2015. "Accounting Research and Common Sense," Abacus, Accounting Foundation, University of Sydney, vol. 51(4), pages 525-535, December.
    29. Stephen T. Ziliak, 2008. "Retrospectives: Guinnessometrics: The Economic Foundation of "Student's" t," Journal of Economic Perspectives, American Economic Association, vol. 22(4), pages 199-216, Fall.
    30. David J. Hand, 2016. "Editorial: ‘Big data’ and data sharing," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(3), pages 629-631, June.
    31. Mingfeng Lin & Henry C. Lucas & Galit Shmueli, 2013. "Research Commentary ---Too Big to Fail: Large Samples and the p -Value Problem," Information Systems Research, INFORMS, vol. 24(4), pages 906-917, December.
    32. Jae H. Kim, 2019. "Tackling False Positives In Business Research: A Statistical Toolbox With Applications," Journal of Economic Surveys, Wiley Blackwell, vol. 33(3), pages 862-895, July.
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