Association is not causation is perhaps the most important lesson one learns in a statistics class. There are many reasons that a variable X can be correlated with a variable Y without having any direct effect on Y. The following comical example underscores that correlation is not causation. It shows a very strong correlation between divorce rates and margarine consumption. The cases presented in the spurious correlation site are all instances of what is generally called data dredging, data fishing, or data snooping. In this chapter, the authors described alternatives to the average and standard deviation that are robust to outliers. There is also an alternative to the sample correlation for estimating the population correlation that is robust to outliers. A very likely possibility is that the children needing regular parental help, receive help because they don’t perform well in school. Confounders are perhaps the most common reason that leads to associations begin misinterpreted.