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A Frequentist Alternative to Significance Testing, p -Values, and Confidence Intervals

Author

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  • David Trafimow

    (Department of Psychology, MSC 3452, New Mexico State University, P.O. Box 30001, Las Cruces, NM 88003-8001, USA)

Abstract

There has been much debate about null hypothesis significance testing, p -values without null hypothesis significance testing, and confidence intervals. The first major section of the present article addresses some of the main reasons these procedures are problematic. The conclusion is that none of them are satisfactory. However, there is a new procedure, termed the a priori procedure (APP), that validly aids researchers in obtaining sample statistics that have acceptable probabilities of being close to their corresponding population parameters. The second major section provides a description and review of APP advances. Not only does the APP avoid the problems that plague other inferential statistical procedures, but it is easy to perform too. Although the APP can be performed in conjunction with other procedures, the present recommendation is that it be used alone.

Suggested Citation

  • David Trafimow, 2019. "A Frequentist Alternative to Significance Testing, p -Values, and Confidence Intervals," Econometrics, MDPI, vol. 7(2), pages 1-14, June.
    • Handle: RePEc:gam:jecnmx:v:7:y:2019:i:2:p:26-:d:237276
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    References listed on IDEAS

    as
    1. 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.
    2. Valentin Amrhein & David Trafimow & Sander Greenland, 2019. "Inferential Statistics as Descriptive Statistics: There Is No Replication Crisis if We Don’t Expect Replication," The American Statistician, Taylor & Francis Journals, vol. 73(S1), pages 262-270, March.
    3. Daniel J. Benjamin & James O. Berger & Magnus Johannesson & Brian A. Nosek & E.-J. Wagenmakers & Richard Berk & Kenneth A. Bollen & Björn Brembs & Lawrence Brown & Colin Camerer & David Cesarini & Chr, 2018. "Redefine statistical significance," Nature Human Behaviour, Nature, vol. 2(1), pages 6-10, January.
      • Daniel Benjamin & James Berger & Magnus Johannesson & Brian Nosek & E. Wagenmakers & Richard Berk & Kenneth Bollen & Bjorn Brembs & Lawrence Brown & Colin Camerer & David Cesarini & Christopher Chambe, 2017. "Redefine Statistical Significance," Artefactual Field Experiments 00612, The Field Experiments Website.
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    Cited by:

    1. Stef, Nicolae & Zenou, Emmanuel, 2021. "Management-to-staff ratio and a firm's exit," Journal of Business Research, Elsevier, vol. 125(C), pages 252-260.
    2. Jae H. Kim, 2022. "Moving to a world beyond p-value," Review of Managerial Science, Springer, vol. 16(8), pages 2467-2493, November.
    3. Trafimow, David & Hyman, Michael R. & Kostyk, Alena & Wang, Cong & Wang, Tonghui, 2021. "The harmful effect of null hypothesis significance testing on marketing research: An example," Journal of Business Research, Elsevier, vol. 125(C), pages 39-44.
    4. Trafimow, David & Hyman, Michael R. & Kostyk, Alena, 2020. "The (im)precision of scholarly consumer behavior research," Journal of Business Research, Elsevier, vol. 114(C), pages 93-101.

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