 To understand what is going on in this example, we need to look more closely at the relationship between sample statistics and underlying probability distributions. We frequently think about this in terms of a classic urn model. An urn contains a certain proportion of black and white balls. We draw balls from the urn, and use the frequency of black balls drawn to estimate the proportion of black balls in the urn. Using statistical frequencies to estimate probabilities in this way typically requires an assumption that the samples are probabilistically independent: drawing a black ball does not affect the probability that the next ball drawn will be black. Sober’s example involves what statisticians call a time series. When we sample Venetian sea levels over the course of years, we are not drawing probabilistically independent samples from a stable probability distribution. If the sea level is high in a particular year, we can predict that the sea level will be similarly high the following year (they tend not to change dramatically from one year to the next). For this reason, we cannot interpret the relative frequencies that we obtain as estimates of an underlying probability distribution. Thus, even though there is a correlation between V and L in our samples, it is impossible to interpret this as a probabilistic correlation with  \(p(V \cap L) &gt; p(V)p(L)\). Not all correlations in statistical samples bespeak probabilistic correlations. (See also Hoover 2003 and Steel 2003 for further discussion of Sober’s example.)