IDEAS home Printed from https://ideas.repec.org/a/gam/jecnmx/v7y2019i2p26-d237276.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2225-1146/7/2/26/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2225-1146/7/2/26/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kim, Jae H., 2017. "Stock returns and investors' mood: Good day sunshine or spurious correlation?," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 94-103.
    2. Patrick Vu, 2022. "Can the Replication Rate Tell Us About Publication Bias?," Papers 2206.15023, arXiv.org, revised Jul 2022.
    3. Wang, Feipeng & Wong, Wing-Keung & Wang, Zheng & Albasher, Gadah & Alsultan, Nouf & Fatemah, Ambreen, 2023. "Emerging pathways to sustainable economic development: An interdisciplinary exploration of resource efficiency, technological innovation, and ecosystem resilience in resource-rich regions," Resources Policy, Elsevier, vol. 85(PA).
    4. Michaelides, Michael, 2021. "Large sample size bias in empirical finance," Finance Research Letters, Elsevier, vol. 41(C).
    5. 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.
    6. Kim, Jae & Choi, In, 2015. "Unit Roots in Economic and Financial Time Series: A Re-Evaluation based on Enlightened Judgement," MPRA Paper 68411, University Library of Munich, Germany.
    7. Fieberg, Christian & Günther, Steffen & Poddig, Thorsten & Zaremba, Adam, 2024. "Non-standard errors in the cryptocurrency world," International Review of Financial Analysis, Elsevier, vol. 92(C).
    8. G. Christopher Crawford & Vitaliy Skorodziyevskiy & Casey J. Frid & Thomas E. Nelson & Zahra Booyavi & Diana M. Hechavarria & Xuanye Li & Paul D. Reynolds & Ehsan Teymourian, 2022. "Advancing Entrepreneurship Theory Through Replication: A Case Study on Contemporary Methodological Challenges, Future Best Practices, and an Entreaty for Communality," Entrepreneurship Theory and Practice, , vol. 46(3), pages 779-799, May.
    9. Todd Mitton, 2022. "Methodological Variation in Empirical Corporate Finance," The Review of Financial Studies, Society for Financial Studies, vol. 35(2), pages 527-575.
    10. Amélie Charles & Olivier Darné & Jae H. Kim, 2022. "Stock return predictability: Evaluation based on interval forecasts," Bulletin of Economic Research, Wiley Blackwell, vol. 74(2), pages 363-385, April.
    11. Stephan B. Bruns & David I. Stern, 2019. "Lag length selection and p-hacking in Granger causality testing: prevalence and performance of meta-regression models," Empirical Economics, Springer, vol. 56(3), pages 797-830, March.
    12. Markku Maula & Wouter Stam, 2020. "Enhancing Rigor in Quantitative Entrepreneurship Research," Entrepreneurship Theory and Practice, , vol. 44(6), pages 1059-1090, November.
    13. Anghel, Dan Gabriel, 2021. "Data Snooping Bias in Tests of the Relative Performance of Multiple Forecasting Models," Journal of Banking & Finance, Elsevier, vol. 126(C).
    14. Beecham, Roger & Lovelace, Robin, 2022. "A framework for inserting visually-supported inferences into geographical analysis workflow: application to road safety research," OSF Preprints mfja8, Center for Open Science.
    15. 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.
    16. Austin Chia & Margaret L. Kern, 2021. "Subjective Wellbeing and the Social Responsibilities of Business: an Exploratory Investigation of Australian Perspectives," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 16(5), pages 1881-1908, October.
    17. Jan S. Krause & Gerrit Nanninga & Patrick Ring & Ulrich Schmidt & Daniel Schunk, 2020. "The Influence of Ambient Temperature on Social Perception and Social Behavior," Working Papers 2013, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    18. Zack Jourdan & J. Ken. Corley & Randall Valentine & Arthur M. Tran, 2023. "Fintech: A content analysis of the finance and information systems literature," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-21, December.
    19. 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.
    20. Kim, Jae, 2015. "How to Choose the Level of Significance: A Pedagogical Note," MPRA Paper 66373, University Library of Munich, Germany.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jecnmx:v:7:y:2019:i:2:p:26-:d:237276. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.