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From Bayes to the Just Noticeable Difference to Effect Sizes: A Note to Understanding the Clinical and Statistical Significance of Oenologic Research Findings*

Published online by Cambridge University Press:  08 June 2012

Domenic V. Cicchetti
Domenic V. Cicchetti
Affiliation:
Yale Home Office, 94 Linsley Lake Road, North Branford, CT 06471; email: dom.cicchetti@yale.edu
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Abstract

The objectives of this paper are (1) to broaden the concept and importance of differentiating statistical significance from clinical or practical significance that was introduced in a recent oenologie application that appeared in this Journal (Cicchetti, 2007); (2) to highlight the major contribution of Economics to the clinical-statistical significance differentiation; (3) to provide oenologie researchers with the tools to accomplish this objective; and (4) to provide examples of oenologie applications using these biostatistical tools. Results indicate that the terms clinical significance, effect sizes, the just noticeable difference between stimuli, and the economic term marginal utility are conceptually related and when applied to the results of oenologie research, offer a richness of interpretation that levels of statistical significance alone cannot provide. (JEL Classification: C0, C1)

Type
Shorter Papers
Information
Journal of Wine Economics , Volume 3 , Issue 2 , Winter 2008 , pp. 185 - 193
Copyright
Copyright © American Association of Wine Economists 2008

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References

Amerine, M.A. and Roessler, E.B. (1983). Wines: Their Sensory Evaluation, 2nd edition, New York: W.H. Freeman.Google Scholar
Bayes, T. (1763). An essay towards solving a problem in the doctrine of chances. Published posthumously in the Transactions of the Royal Society of London, 53, 370–418, and 54, 296–325.Google Scholar
Borenstein, M. (1998). The shift from significance testing to effect size estimation. In Bellak, A.S., Hersen, M. (Series eds.) and Schooler, N. (Vol. ed.), Research and Methods: Comprehensive Clinical Psychology (vol. 3, pp. 313–349). New York: Pergamon.CrossRefGoogle Scholar
Borenstein, M., Rothstein, H. and Cohen, J. (2001). Power and precision: A computer program for statistical power analysis and confidence intervals. Englewood, NJ: Biostat, Inc.Google Scholar
Cicchetti, D.V., Bronen, R., Spencer, S., Haut, S., Berg, A., Oliver, P. and Tyrer, P. (2006). Rating scales, scales of measurement, issues of reliability: Resolving some critical issues for clinicians and researchers. Journal of Nervous and Mental Disease, 194, 557–564.CrossRefGoogle ScholarPubMed
Cicchetti, D.V. (1994). Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychological Assessment, 6, 284–290.CrossRefGoogle Scholar
Cicchetti, D.V. (2007). Assessing the reliability of blind wine tasting: Differentiating levels of clinical and statistical meaningfulness. Journal of Wine Economics, 2, 196–202.CrossRefGoogle Scholar
Cicchetti, D.V. and Sparrow, S.S. (1981). Developing criteria for establishing interrater reliability of specific items: applications to assessment of adaptive behavior. American Journal of Mental Deficiency, 86, 127–137.Google Scholar
Cohen, J. (1988). Statistical power analysis for the behavioral sciences. 2nd edition. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Fechner, G.T. (1907). Elemente der Psychophysik I u. II. Leipzig: Breitkopf & Härtel.Google Scholar
Fleiss, J.L. (1981). Statistical Methods for Rates and Proportions. 2nd edition. New York: Wiley.Google Scholar
Fleiss, J.L., Levin, B., and Paik, M.C. (2003). Statistical methods for rates and proportions. 3rd edition. New York: Wiley.CrossRefGoogle Scholar
Landis, J.R. and Koch, G.G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159–174.Google Scholar
McCarthy, P.L., Cicchetti, D. V., Sznajderman, S.D., Forsyth, B.C., Baron, M.A., Fink, H.D., Czarkowski, N., Bauchner, H. and Lustman-Findling, K. (1991). Demographic, clinical and psychosocial predictors of the reliability of mothers' clinical judgments. Pediatrics, 88, 1041–1046.CrossRefGoogle ScholarPubMed
Neyman, J. and Pearson, E.S. (1928). On the use and interpretation of certain test criteria for purposes of statistical inference. Biometrika, 20A, 175–240 and 263–294.Google Scholar
Neyman, J. and Pearson, E.S. (1933). On the problem of the most efficient tests of statistical hypotheses. Transactions of the Royal Society of London Series A, 231, 289–337.Google Scholar
Nunnally, J.C. (1978). Psychometric Theory. New York: McGraw-Hill.Google Scholar
Prilaid, D.A. (2007). The placebo of place: Terroir effects in the blind and sighted quality assessments of South African varietal wines. Journal of Wine Research, 18, 87–105.CrossRefGoogle Scholar
Stevens, S.S. (1951). Mathematics, measurement and psychophysics. In Stevens, S.S. (ed.), Handbook of Experimental Psychology. New York: Wiley, (Chapter 1, 1–49).Google Scholar
Stone, H. and Sidel, J.L. (eds.) (1993). Sensory Evaluation Practices. 2nd edition. New York: Academic Press.Google Scholar
Wieser, F. von (1893). Natural Value. New York, New York: MacMillan (English ed.).Google Scholar
Wikipedia (2008). Just noticeable difference. http://en.wikipedia.org/wiki/Just_noticeable_difference (accessed, Dec 20, 2008)Google Scholar