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)

Rank preference and mixture models have been employed to evaluate the ranks assigned by consumers in taste tests of beans, cheese, crackers, salad dressings, soft drinks, sushi, animal feed, and wine. In many wine tastings, including the famous 1976 Judgment of Paris and the 2012 Judgment of Princeton, judges assign scores rather than ranks, and those scores often include ties. This article advances the application of ranking and mixture models to wine-tasting results by modifying the established use of a Plackett-Luce rank preference model to accommodate scores and ties. The modified model is tested and then employed to evaluate the Paris and Princeton wine-tasting results. Test results show that the mixture model is an accurate predictor of observed rank densities. Results for Paris and Princeton show that the group preference orders implied by the mixture model are highly correlated with the orders implied by widely employed rank-sum methods. However, the mixture model satisfies choice axioms that rank-sum methods do not, it yields an estimate of the proportion of scores that appear to be assigned randomly, and it also yields a preference order based on nonrandom preferences that tasters appear to hold in common. (JEL Classifications: A10, C00, C10, C12, D12)

Evaluating observed wine-tasting results as a mixture distribution, using linear regression on a transformation of observed results, has been described in the wine-tasting literature. This article advances the use of mixture models by considering that existing work, examining five analyses of ranking and mixture model applications to non-wine food tastings and then deriving a mixture model with specific application to observed wine-tasting results. The mixture model is specified with Plackett-Luce probability mass functions, solved with the expectation maximization algorithm that is standard in the literature, tested on a hypothetical set of wine ranks, tested with a random-ranking Monte Carlo simulation, and then employed to evaluate the results of a blind tasting of Pinot Gris by experienced tasters. The test on a hypothetical set of wine ranks shows that a mixture model is an accurate predictor of observed rank densities. The Monte Carlo simulation yields confirmatory results and an estimate of potential Type I errors (the probability that tasters appear to agree although ranks are actually random). Application of the mixture model to the tasting of Pinot Gris, with over a 95% level of confidence based on the likelihood ratio and t statistics, shows that agreement among tasters exceeds the random expectation of illusory agreement. (JEL Classifications: A10, C10, C00, C12, D12)