 Observational studies often begin by reporting a recorded correlation between a medical outcome of interest and one or a set of independent variables: lung cancer rates are higher in groups of smokers than in groups of non-smokers, liver cancer rates are higher in populations that tend to consume food that has been contaminated with aflatoxin than in populations whose food is uncontaminated, to give a few examples. That smoking causes lung cancer, or aflatoxin cancer of the liver, would indeed account for the observed correlations. But so would a variety of other hypotheses. Generally, if two variables X and Y are correlated, it may be the case that X causes Y, Y causes X or a common factor Z causes both X and Y (or a combination of these). In the smoking/lung cancer case, all three hypotheses were invoked as possible accounts of the data. Ronald Fisher famously proposed that it may be the case that early stages of bronchial carcinoma cause an individual to crave cigarettes, and he provided some evidence that both smoking behavior and susceptibility to lung cancer have a common genetic basis (Fisher 1958). Moreover, it is possible that the correlation itself is spurious—that the data are correlated as per some measure of correlation such as Pearson’s coefficient, but that the underlying variables are not in fact correlated in the population of interest. Selection bias is normally understood as the bias that obtains when individuals self-select into the observed population and the reasons for which they do so are correlated with the outcome variable. If an observational study examines only hospitalized patients and smokers are more likely to be in hospital for reasons that have nothing to do with lung cancer, then smoking and lung cancer can be correlated in the data even if the variables are independent in the general population. Mismeasurement and diagnostic error provide another account of spurious correlation. Suppose tuberculosis was on the rise a generation or so after many people traded pipe smoking for cigarette smoking. Then, if it was difficult to distinguish a death from tuberculosis from a death from lung cancer because necropsy techniques were not sufficiently well developed, the data might again show a correlation even though the population variables are uncorrelated.