In observational studies, confounding variables are factors that confuse or obscure the association between a primary exposure of interest and an outcome.
Deeper dive
For example, scientists studying the relationship between birth order and Down syndrome found that later born children had much higher risks of Down syndrome than first-born children. When they delved deeper into the association, however, they found much of that risk was explained by maternal age. Mothers over age 40 were far more likely to have babies born with Down syndrome than younger mothers. At the same time, mothers having a third, fourth, or fifth child are also more likely to be older. Therefore, the association between birth order and Down syndrome was confounded by maternal age.
Confounding is very common, and it is not always easy to tease out or control for in observational studies. It’s the main reason that randomized, controlled trials are considered to a higher level of evidence than observational studies.
For some examples, see “Don’t fudge the facts on chocolate studies” and this column, “Epidemiology and how confounding statistics can confuse,” by Marya Zilberberg, M.D., M.P.H.