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Changing statistical significance with the amount of information: The adaptive α significance level

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  • Pérez, María-Eglée
  • Pericchi, Luis Raúl

Abstract

We put forward an adaptive alpha which changes with the amount of sample information. This calibration may be interpreted as a Bayes–non-Bayes compromise, and leads to statistical consistency. The calibration can also be used to produce confidence intervals whose size takes in consideration the amount of observed information.

Suggested Citation

  • Pérez, María-Eglée & Pericchi, Luis Raúl, 2014. "Changing statistical significance with the amount of information: The adaptive α significance level," Statistics & Probability Letters, Elsevier, vol. 85(C), pages 20-24.
    • Handle: RePEc:eee:stapro:v:85:y:2014:i:c:p:20-24
    DOI: 10.1016/j.spl.2013.10.018
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    References listed on IDEAS

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    1. John P A Ioannidis, 2005. "Why Most Published Research Findings Are False," PLOS Medicine, Public Library of Science, vol. 2(8), pages 1-1, August.
    2. F. Javier Girón & M. Lina Martínez & Elías Moreno & Francisco Torres, 2006. "Objective Testing Procedures in Linear Models: Calibration of the p‐values," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(4), pages 765-784, December.
    3. Sellke T. & Bayarri M. J. & Berger J. O., 2001. "Calibration of rho Values for Testing Precise Null Hypotheses," The American Statistician, American Statistical Association, vol. 55, pages 62-71, February.
    4. James Berger & M. J. Bayarri & L. R. Pericchi, 2014. "The Effective Sample Size," Econometric Reviews, Taylor & Francis Journals, vol. 33(1-4), pages 197-217, June.
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    Cited by:

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    2. Huynh, Thanh D. & Nguyen, Thu Ha & Truong, Cameron, 2020. "Climate risk: The price of drought," Journal of Corporate Finance, Elsevier, vol. 65(C).
    3. 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.
    4. D. Vélez & M. E. Pérez & L. R. Pericchi, 2022. "Increasing the replicability for linear models via adaptive significance levels," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(3), pages 771-789, September.
    5. Liu, Huajin & Zhang, Wei & Zhang, Xiaotao & Liu, Jia, 2021. "Temperature and trading behaviours," International Review of Financial Analysis, Elsevier, vol. 78(C).
    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. 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.

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