Chapter 8. Emotion and Motivation

Statistical Lesson. The important thing to remember about relationships between variables is that we cannot conclude that one of the variables caused the change we see in the other variable. This is especially true when we do not conduct an experiment that included manipulation or control over the variables or environment being studied. We cannot say conclusively that the variables affected each other. This is what statisticians are referring to when they say “do not infer causality.” Just because there is a relationship or pattern between two variables, such as they both increase, both decrease, or one increases while the other decreases, this does not mean that one causes the other. Most likely there is another third, or what we call confounding, variable that could be contributing to the pattern we observe.

A popular example is the relationship between ice cream sales and violent acts. A researcher in the 1980s found that in New York City as ice cream sales went up, so did violent acts. Though we might jump to the conclusion that ice cream causes people to be more violent, and subsequently ban ice cream everywhere, we cannot nor should not do this. When collecting naturally occurring data without the use of a true experiment, we cannot infer that one of the variables cause the others. What might be contributing to this relationship? Well, people tend to buy more ice cream when it is hot outside or in the summer time, and this is also the time when more crime occurs. Could it be that heat or being outside more is what links these two variables together? Keep this in mind when interpreting relationship – do not infer causality, and always look for confounding variables.