If you are a student of statistics, you may already know what a ‘confounder’ or “confounding variable” is.

The textbook definition (as found in StatisticsHowTo.com): “In an experiment, the independent variable typically has an effect on your dependent variable. For example, if you are researching whether lack of exercise leads to weight gain, lack of exercise is your independent variable and weight gain is your dependent variable. Confounding variables are any other variable that also has an effect on your dependent variable.”

Got it? No? Okay, here’s an example (edited) that should help. It’s from  Bob Spunt writing at Psychology in Action

It is known that throughout the year, murder rates and ice cream sales are highly positively correlated. That is, as murder rates rise, so does the sale of ice cream. There are three possible explanations for this correlation:

Possibility #1: Murders cause people to purchase ice cream.

Possibility #2: Purchasing ice cream causes people to murder or get murdered.

Possibility #3: There is a third variable — a confounding variable — which causes the increase in BOTH ice cream sales AND murder rates. For instance, the weather. When it’s hot and summery, people spend more time outside interacting with each other, and hence are more likely to get into the kinds of situations that lead to murder.

In this example, the weather is a variable that confounds the relationship between ice cream sales and murder rates. You may also recognize this as the so-called third variable problem, which refers to the fact that any time we observe a relationship among two variables, there’s always the possibility that some third variable that we don’t know about is responsible for (“confounding”) the relationship.

— Return to A New Reason to Retire Later: You’ll Live Longer.