In this paper we examine the hypothesis that aeroplane accidents are grouped, ie that individual accidents are not randomly spaced in time. While it has not been possible to find any explicit statements of the non-randomness hypothesis, it seems to be a widely-held belief. In its least precise form, the belief is that as soon as there is one aeroplane accident, there will be “several more within a relatively short period of time”. Clearly one does not expect equal or even near-equal spacing between aircraft accidents; the interesting question is what amount of bunching leads to the rejection of randomness as an adequate description of the underlying distribution. And if non-randomness is encountered in practice, how is it explained or interpreted?

Wine tastings inevitably involve some form of grading or ranking the wines, since the objective of tastings is to determine which wine is best, second best, etc., at least among the tasters on that particular occasion. Much has been written about the care that has to be taken that judges are not influenced by extraneous and irrelevant factors and that they do not influence one another. Ultimately, of course, the views of the judges need to be congealed in a single ranking that expresses the “social preference” among the wines. And therein lies the rub: how to aggregate individual preferences into a social ranking.

The paper has three basic objectives: (1) to discuss and analyze the subtleties of ranking wines in blind tastings, (2) to analyze the degree of agreement or disagreement among the tasters (judges) and (3) to shed some light on the problem of identifying the wines and to determine when the identifications of the judges might be called statistically significant. The first issue utilizes the rank sums or the related measure, “votes against,” and discusses the appropriateness of a statistical test introduced by Kramer. The second introduces Kendall's W coefficient of concordance and discusses some other, related measures. The third derives the finite sample distribution of the number of correct identifications under the null hypothesis of random identifications, from which critical values can be obtained, both for the case in which each wine has to be identified exactly and the case in which there is a small number of different types of wine that have to be identified. (JEL Classification numbers: C12, C15, C49, C59)

In wine tastings, in which several tasters (judges) taste several wines, it is important to insure objectivity to the extent possible. This is usually accomplished by holding the tasting “blind,” i.e., covering the bottles so that the tasters do not know which wine is in which bottle. At some agreed upon point in the proceedings, the tasters reveal what they think about the various bottles. Ideally, this revelation would take place by secret ballot, lest a taster's choices be influenced by what he or she hears another taster say. But in any event, there are two standard ways of rating the wines. The older method is to assign them “grades” on a scale of, say, up to 100 points (Parker) or up to 20 points as in the famous face-off between California wines and French Bordeaux wines in 1976 (see Ashenfelter et al., 2007). As Ashenfelter at al. point out, this has the distinct disadvantage that a judge with greater dispersion in his or her grades will have a greater influence on the average score that each wine achieves.

The inspiration for the exegesis that follows comes directly from Harry G. Frankfurt, distinguished emeritus professor of philosophy at Princeton University, who recently published an enormously successful essay on the subject of bullshit (Frankfurt, 2005). In the author's view,

“One of the most salient features of our culture is that there is so much bullshit. Everyone knows this. Each of us contributes his share. But we tend to take the situation for granted. Most people are rather confident of their ability to recognize bullshit and to avoid being taken in by it. So the phenomenon has not aroused much deliberate concern. We have no clear understanding of what bullshit is, why there is so much of it, or what functions it serves.” (Frankfurt, 2005, p. 1)

A fine philosophical insight into bullshit is provided by Bernie Laplante, played by Dustin Hoffman in the movie “Hero.”

Among all the social sciences, the art of model building is perhaps best developed in economics.Models or theories are conceptualizations of and abstractions from reality; they rely intrinsically on ldquo;if-thenrdquo;statements and should lead to testable propositions if they are to be of any use at all.

It would be difficult to give a brief description of all the important characteristics of economic models, for there are numerous types, designed to describe or explain completely different aspects of economic reality. Most of them contain as essential ingredients some statements about the technical environment (e.g., marginal cost curves rise after a certain point) and some statements about human motivation (e.g., the entrepreneur desires to maximize profits; the consumer maximizes utility).The models themselves can have either descriptive or normative use. The Keynesian model or theory “explains” the factors diat cause the level of national income to be what it is and allows one to deduce the direction of change in national income resulting from changes in the parameters. The usefulness of such a descriptive model is determined by whether or not its conclusions are testable propositions. Normative models, most frequently encountered in economics in welfare economics and in operations research, also utilize statements about environment and motivations but do not pretend to predict; infact they merely prescribe. The test of the usefulness of such normative models is whether ornot their prescriptions are at least in principle imple—mentable.