Hostname: page-component-8448b6f56d-tj2md Total loading time: 0 Render date: 2024-04-24T20:54:09.272Z Has data issue: false hasContentIssue false

Wine, Women, Men, and Type II Error*

Published online by Cambridge University Press:  18 May 2017

Jeffrey C. Bodington*
Affiliation:
Bodington & Company, 50 California St. #630, San Francisco, CA 94111; email: jcb@bodingtonandcompany.com

Abstract

More than forty published works show that women and men differ in their taste preferences for sweet, salt, sour, bitter, fruit, and other flavors. Despite those differences, dozens of state fair and other wine competitions determine winners' ribbons, medals, scores, and ranks by pooling the opinions of female and male judges. This article examines twenty-three blind wine tastings during which female and male judges scored more than nine hundred wines. Two-sample t-test results show that the gender-specific distributions of scores do have similar means and standard deviations. Exact p-values for two-sample chi-square tests show that the distributions of men's and women's scores are not significantly different, and exact p-values for likelihood ratio tests of Plackett-Luce model results show that the genders' preference orders are not significantly different. The correlation coefficient between women's and men's scores is weakly positive in 90 percent of the tastings. On that evidence, indications that the genders prefer different wines are difficult to detect. If such differences do exist, as the nonwine literature implies, the results of this analysis show that those differences are small compared to non-gender-related idiosyncratic differences between individuals and random expressions of preference. The potential for accept-a-false-null-hypothesis Type II error when pooling female and male judges' wine-related opinions appears to be small. (JEL Classifications: A10, C10, C00, C12, D12)

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

*

The author thanks Professor Manuel Malfeito Ferreira of Instituto Superior de Agronomia, Universidade de Lisboa, for providing certain wine-tasting results; Professor Domenic Chicchetti of Yale University for encouraging consideration of correlation coefficients; and an anonymous reviewer for perceptive and constructive comments. All remaining errors are the responsibility of the author alone.

References

Alvo, M., and Yu, P. L. H. (2014). Statistical Methods for Ranking Data. New York: Springer.CrossRefGoogle Scholar
Anderson, R. L. (1959). Use of Contingency Tables in the Analysis of Consumer Preference Studies. Biometrics, 15(4), 582590.Google Scholar
Ashenfelter, O., and Storchmann, K. (2012). The Judgment of Princeton and Other Articles [Editorial]. Journal of Wine Economics, 7(2), 139142.Google Scholar
Atkin, T., and Sutanonpaiboon, J. (2007). A Multinational Study of Gender Wine Preferences. Presented at International Decision Sciences Institute / Asia and Pacific DSI.Google Scholar
Bagui, S., and Bagui, S. C. (2005). An Algorithm and Code for Computing Exact Critical Values for Freidman's Nonparametric ANOVA. Journal of Modern Applied Statistical Methods, 4(1), 312318.Google Scholar
Bartoshuk, L. M. (1993). The Biological Basis of Food Perception and Acceptance. Food Quality and Preference, 4(1), 2132.Google Scholar
Bartoshuk, L. M. (2000). Comparing Sensory Experiences across Individuals: Recent Psychophysical Advances Illuminate Genetic Variation in Taste Perception. Chemical Senses, 25(4), 447460.Google Scholar
Bodington, J. (2012). 804 Tastes: Evidence on Randomness, Preferences and Value from Blind Tastings. Journal of Wine Economics, 7(2), 181191.CrossRefGoogle Scholar
Bodington, J. (2015a). Evaluating Wine-Tasting Results and Randomness with a Mixture of Rank Preference Models. Journal of Wine Economics, 10(1), 3146.Google Scholar
Bodington, J. (2015b). Testing a Mixture of Rank Preference Models on Judges’ Scores in Paris and Princeton. Journal of Wine Economics, 10(2), 173189.Google Scholar
Cao, J. (2014). Quantifying Randomness versus Consensus in Wine Quality Ratings. Journal of Wine Economics, 9(2), 202213.Google Scholar
Cicchetti, D. V. (2006). The Paris 1976 Wine Tastings Revisited Once More: Comparing Ratings of Consistent and Inconsistent Tasters. Journal of Wine Economics, 1(2), 125140.Google Scholar
Cochran, W. G. (1952). The Chi-Square Test of Goodness of Fit. Annals of Mathematical Statistics, 23(3), 315345.Google Scholar
Corbin, C. (2006). Sex Differences in Taste Preferences in Humans. Literature review for Psychology 451 under D. Pittman, Wofford College, Spartanburg, SC.Google Scholar
Crawshaw, J., and Chambers, J. (2001). A Concise Course in Advanced Level Statistics. Cheltenham, UK: Nelson Thornes.Google Scholar
Critchlow, D. E. (1985). Metric Methods for Analyzing Partially Ranked Data. New York: Springer-Verlag.Google Scholar
Friedman, M. (1937). The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance. Journal of the American Statistical Association, 32(200), 675701.Google Scholar
Hanni, T., and Utermohlen, V. (2010). Attitudes and Behavior of “Sweet” vs. “Tolerant” Wine Consumers. Results of an online consumer survey sponsored by the Consumer Wine Awards at Lodi with Lodi Tokay Rotary and Diversity Wine Awards LLC.Google Scholar
Hulkower, N. D. (2009). The Judgment of Paris According to Borda. Journal of Wine Research, 20(3), 171182.Google Scholar
Jackson, R. S. (2009). Wine Tasting: A Professional Handbook. 2nd ed. Amsterdam: Elsevier Academic Press.Google Scholar
Khazanie, R. (1996). Statistics in a World of Applications. New York: HarperCollins College Publishers.Google Scholar
Laeng, B., Berridge, K. C., and Butter, C. M. (1993). Pleasantness of a Sweet Taste during Hunger and Satiety: Effects of Gender and “Sweet Tooth.Appetite, 21(3), 247254.Google Scholar
Marden, J. I. (1995). Analyzing and Modeling Rank Data. London: Chapman and Hall.Google Scholar
McDonald, J. H. (2014). Handbook of Biological Statistics. 3rd ed. Baltimore: Sparky House Publishing.Google Scholar
Olkin, I., Lou, Y., Stokes, L., and Cao, J. (2015). Analyses of Wine-Tasting Data: A Tutorial. Journal of Wine Economics, 10(1), 430.CrossRefGoogle Scholar
Ough, C. S., and Amerine, M. A. (1970). Effect of Subjects’ Sex, Experience, and Training on Their Red Wine Color-Preference Patterns. Perceptual Motor Skills, 30(2), 395398.Google Scholar
Pearson, K. (1900). On the Criterion That a Given System of Deviations from the Probable in the Case of a Correlated System of Variables Is Such That It Can Be Reasonably Supposed to Have Arisen from Random Sampling. Philosophical Magazine, 50(302), 157175.Google Scholar
Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P. (1997). Numerical Recipes in Fortran 77: The Art of Scientific Computing. New York: Cambridge University Press.Google Scholar
Rayner, J. C. W., and Best, D. J. (1990). A Comparison of Some Rank Tests Used in Taste-Testing. Journal of the Royal Society of New Zealand, 20(3), 269272.CrossRefGoogle Scholar
Shrout, P. E., and Fleiss, J. L. (1979). Intraclass Correlations: Uses in Assessing Rater Reliability. Psychological Bulletin, 86(2), 420428.Google Scholar
Thach, L., and Chang, K. (2015). 2015 Survey of American Wine Consumer Preferences. Available from www.winebusiness.com, Wine Communications Group, Sonoma, CA.Google Scholar