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:
- Azzalini, A. and Valle, A.D. (1996), “The multivariate skew normal distribution”, Biometrika, Vol. 83 No. 4, pp. 715-726, doi: 10.1093/biomet/83.4.715.
- Hubbard, R. (2016), Corrupt Research: the Case for Reconceptualizing Empirical Management and Social Science, Sage Publications, Los Angeles, CA.
- Hyman, M.R., Ganesh, G. and McQuitty, S. (2002), “Augmenting the household affluence construct”, Journal of Marketing Theory and Practice, Vol. 10 No. 3, pp. 13-31, doi: 10.1080/10696679.2002.11501917.
- McQuitty, S. (2004), “Statistical power and structural equation models in business research”, Journal of Business Research, Vol. 57 No. 2, pp. 175-183, doi: 10.1016/S0148-2963(01)00301-0.
- McQuitty, S. (2018), “Reflections on ‘Statistical power and structural equation models in business research’”, Journal of Global Scholars of Marketing Science, Vol. 28 No. 3, pp. 272-277, doi: 10.1080/21639159.2018.1434806.
- 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.
- 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
- 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.
- 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.
- 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.
- 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.