Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-26T23:01:15.561Z Has data issue: false hasContentIssue false

Opinions versus Facts: A Bio-statistical Paradigm Shift in Oenological Research*

Published online by Cambridge University Press:  02 August 2017

Dom Cicchetti*
Affiliation:
Yale University School of Medicine, Department of Biometry, Yale University Home Office, Box 317, North Branford, CT 06471; e-mail: [email protected].

Abstract

A substantial oenological literature exists on opinions of experts and neophytes as they relate to opinions about the quality of wines (Ashenfelter and Quandt, 1998; Cicchetti, 2004; Lindley, 2006). These opinions can be contrasted with factual binary questions about wine: Is it oaked? Does it contain sulfites? Is it filtered? Is the grape varietal Cabernet Sauvignon or Cabernet Franc? Syrah or Grenache? Pinot Noir or Gamay? Such factual binary issues are examined within the broader context of the various measures of factual judgment: Overall Accuracy (OA), Sensitivity (Se), Specificity (Sp), Predicted Positive Accuracy (PPA), and Predicted Negative Accuracy (PNA). The resulting biostatistical methodology derives from biobehavioral diagnostic research investigations. The purpose of this report is to apply this methodology to the discipline of oenology to compare wine judgments with wine facts. Using hypothetical examples, wine judges’ classifications of wines as oaked or unoaked were analyzed for their degree of accuracy. The results show that OA is a poor measure of the accuracy of binary judgments relative to Se, Sp, PPA, or PNA. The biostatistics of the problem could have wide-ranging applications in the design of future oenological research investigations, and in scientific research more broadly. (JEL Classifications: C1, L15, Q13)

Type
Articles
Copyright
Copyright © American Association of Wine Economists 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

A brief summary of this research was presented by the author at the 2016 meeting of the American Association of Wine Economists (AAWE) in Bordeaux, France.

References

Ashenfelter, O., and Quandt, R. (1998). Analyzing a wine tasting statistically. Chance, 12(3), 1620.CrossRefGoogle Scholar
Cicchetti, D. V. (1992). Neural networks and diagnosis in the clinical laboratory: State of the art. Clinical Chemistry, 38, 910.CrossRefGoogle ScholarPubMed
Cicchetti, D. V. (2004). Who won the 1976 blind tasting of French Bordeaux and U.S. Cabernets? Parametrics to the rescue. Journal of Wine Research, 15, 211220.CrossRefGoogle Scholar
Cicchetti, D. V. (2008). From Bayes to the just noticeable difference to effect sizes: A note to understanding the clinical and statistical significance of oenological research findings. Journal of Wine Economics, 3, 185193.CrossRefGoogle Scholar
Cicchetti, A. F., and Cicchetti, D. V. (2008). The balancing act in consistent wine tasting and wine appreciation: A tale told by two brothers. Part I. Consistency in wine tasting and appreciation: A personal-experiential perspective. Journal of Wine Research, 19, 115121.CrossRefGoogle Scholar
Cicchetti, D. V., and Cicchetti, A. F. (2009). Wine rating scales: Assessing their utility for producers, consumers, and oenologic researchers. International Journal of Wine Research, 1, 7383.CrossRefGoogle Scholar
Cicchetti, D. V., and Cicchetti, A. F. (2014). Two oenological titans rate the 2009 Bordeaux wines. Wine Economics and Policy, 3(1), 2836.CrossRefGoogle Scholar
Cicchetti, D. V., Klin, A., and Volkmar, F. R. (2017). Assessing binary diagnoses of bio-behavioral disorders: The clinical relevance of Cohen's Kappa. Journal of Nervous and Mental Disease, 205(1), 5865.CrossRefGoogle ScholarPubMed
Cicchetti, D. V., Volkmar, F. R., Klin, A., and Showalter, D. (1995). Diagnosing autism using ICD-10 criteria: A comparison of neural networks and standard multivariate procedures. Child Neuropsychology, 1(1), 2637.CrossRefGoogle Scholar
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Cortez, P., Cerdeira, A., Almeida, F., Matos, T., and Reis, J. (2009). Modeling wine preferences by data mining from physicochemical properties. Decision Support Systems, 47(4), 547553.CrossRefGoogle Scholar
Feinstein, A. R. (1987). Clinimetrics. New Haven, CT: Yale University Press.CrossRefGoogle Scholar
Fletcher, J. M., Rice, W. I., and Ray, R. M. (1978). Linear discriminant function analysis in neuropsychological research: Some uses and abuses. Cortex, 14, 564577.CrossRefGoogle ScholarPubMed
Frøst, M. B., and Noble, A. (2002). Preliminary study of the effect of knowledge and sensory expertise on liking for red wines. American Journal of Enology and Viticulture, 53(4), 275284.CrossRefGoogle Scholar
Goode, J. (2014). The Science of Wine: From Vine to Glass. 2nd ed. Berkeley: University of California Press.Google Scholar
Kraemer, H. C., and Thiemann, S. (1987). How Many Subjects? Statistical Power Analysis in Research. Newbury Park, CA: Sage.Google Scholar
Lindley, D. V. (2006). Analysis of a wine tasting. Journal of Wine Economics, 1(1), 3341.CrossRefGoogle Scholar
Nachev, A., and Hogan, M. (2013). Using data mining techniques to predict product quality from physicochemical data. Proceedings of the International Conference on Artificial Intelligence, 1, 308314.Google Scholar
Versi, E. (1992). “Gold standard” is an appropriate term. British Medical Journal, 305, 187 (Letter to the Editor).CrossRefGoogle ScholarPubMed