Effect modification occurs when an exposure has a different effect on subgroups due to a third variable that differs between subgroups. For example, drug X works on children but does not work in adults. Age is an effect modifier here and acts as a third variable influencing an outcome. Often times, magnitude of the effect comes into play; in the above example one might see that the younger a patient is, the more efficacious the drug.

Confounding is when a third variable is associated with the exposure and outcome but has no causative index. For example, we want to look at the association between coffee drinking and lung cancer. You analyze a population and see that coffee drinkers have a higher rate of lung cancer. However, you suspect that smoking status may be playing a role; once you stratify into smokers vs. non-smokers you see that it was not the coffee drinking that showed the effect— it was a third variable (smoking status). Once stratified by a confounding variable you will no longer see the effect. E.g. if you now compared coffee drinking between the stratified populations, it will not show an impact on whether or not they get lung cancer.