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Effect size for comparing two or more normal distributions based on maximal contrasts in outcomes

Author

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  • Yan Ling
  • Paul Nelson

Abstract

Effect size is a concept that can be especially useful in bioequivalence and studies designed to find important and not just statistically significant differences among responses to treatments based on independent random samples. We develop and explore a new effect size related to a maximal superiority ordering for assessing the separation among two or more normal distributions, possibly having different means and different variances. Confidence intervals and tests of hypothesis for this effect size are developed using a p value obtained by averaging over a distribution on variances. Since there is almost always some difference among treatments, instead of the usual hypothesis test of exactly no effect, researchers should consider testing that an appropriate effect size has at least, or at most, some meaningful magnitude, when one is available, possibly established using the framework developed here. A simulation study of type I error rate, power and interval length is presented. R-code for constructing the confidence intervals and carrying out the tests here can be downloaded from Author’s website. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Yan Ling & Paul Nelson, 2014. "Effect size for comparing two or more normal distributions based on maximal contrasts in outcomes," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(3), pages 381-399, August.
    • Handle: RePEc:spr:stmapp:v:23:y:2014:i:3:p:381-399
    DOI: 10.1007/s10260-014-0254-y
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    References listed on IDEAS

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    1. Rand R. Wilcox & Tian S. Tian, 2011. "Measuring effect size: a robust heteroscedastic approach for two or more groups," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(7), pages 1359-1368, May.
    2. Browne, Richard H., 2010. "The t-Test p Value and Its Relationship to the Effect Size and P(X>Y)," The American Statistician, American Statistical Association, vol. 64(1), pages 30-33.
    3. Xie R. & Nelson P.I., 2003. "Separation Among Distributions Related by Linear Regression," The American Statistician, American Statistical Association, vol. 57, pages 33-36, February.
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