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Volume 7 - Number 3 | November 2023

Gain-probability diagrams as an alternative to significance testing in economics and finance

David Trafimow, Ziyuan Wang, Tingting Tong, and Tonghui Wang

25/11/2023 93 0 0
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Mathematical and quantitative methods Gain-probability diagrams Skew normal distributions Delta skew lognormal distributions Decision-making Probabilities

Abstract:

Purpose
The purpose of this article is to show the gains that can be made if researchers were to use gain-probability (G-P) diagrams.

Design/methodology/approach
The authors present relevant mathematical equations, invented examples and real data examples.

Findings
G-P diagrams provide a more nuanced understanding of the data than typical summary statistics, effect sizes or significance tests.

Practical implications
Gain-probability diagrams provided a much better basis for making decisions than typical summary statistics, effect sizes or significance tests.

Originality/value
G-P diagrams provide a completely new way to traverse the distance from data to decision-making implications.

References:

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  2. Hubbard, R. (2016), Corrupt Research: the Case for Reconceptualizing Empirical Management and Social Science, Sage Publications, Los Angeles, CA.
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  6. Tong, T., Wang, T., Trafimow, D. and Wang, C. (2022), “The probability of being better or worse off, and by how much, depending on experimental conditions with skew normal populations”, in SriboonchittaKreinovich, W.Y.E.S.V. (Ed.), Credible Asset allocation, Optimal Transport Methods, and Related Topics (141-149), Springer-Verlag.
  7. Trafimow, D. (2019), “A frequentist alternative to significance testing, p-values, and confidence intervals”, Econometrics, Vol. 7 No. 2, pp. 1-14, available at: https://www.mdpi.com/2225-1146/7/2/26
  8. Trafimow, D., Hyman, M.R., Kostyk, A., Wang, C. and Wang, T. (2021), “The harmful effect of null hypothesis significance testing on marketing research: an example”, Journal of Business Research, Vol. 125, pp. 39-44, doi: 10.1016/j.jbusres.2020.11.069.
  9. Trafimow, D., Hyman, M.R., Kostyk, A., Wang, Z., Tong, T., Wang, T. and Wang, C. (2022), “Gain-probability diagrams in consumer research”, International Journal of Market Research, Vol. 64 No. 4, pp. 470-483, doi: 10.1177/14707853221085509.
  10. Wang, Z., Wang, T., Trafimow, D. and Xu, Z. (2022), “A different kind of effect size based on samples from two populations with delta log-skew-normal distributions”, in Ngoc Thach, N., Ha, D.T., Trung, N.D. and Kreinovich, V. (Eds), Prediction and Causality in Econometrics and Related Topics. ECONVN 2021. Studies in Computational Intelligence, Springer, Cham, Vol. 983, doi: 10.1007/978-3-030-77094-5_10.
  11. Ziliak, S.T. and McCloskey, D.N. (2016), The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives, The University of Michigan Press, Ann Arbor, MI.

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