One goal of statistics is to identify relations among variables. What happens to one variable as another variable changes? Does a change in one variable cause a change in another variable? These questions can lead to powerful methods of predicting future values through linear regression. It is important to note the true meaning and scope of correlation, which is the nature of the relation between two variables. Correlation does not allow us to say that there is any causal link between the two variables. In other words, we cannot say that one variable causes another; however, it is not uncommon to see such use in the news media. An example is shown below.

Here we see that, at least visually, there appears to be a relation between the divorce rate in Maine and the per capita consumption of margarine. Does this data imply that all married couples in Maine should immediately stop using margarine to stave off divorce? Common sense tells us that is probably not true. This is an example of a spurious correlation in which there appears to be a relation between the divorce rate and margarine consumption, but it is not a causal link.