Two variables are confounded if they vary together in such a way that it is impossible to determine which variable is responsible for an observed effect. For example, consider an experiment in which two treatments for depression were compared. Treatment one was given to teenaged girls and treatment two was given to middle age women. If a difference between treatments were found, it would be impossible to tell if one treatment were more effective than the other or if treatments for depression are more effective for one age group than the other. Age and treatment are confounded. Naturally, no competent experimenter would design an experiment like that. However, some confounding is much more subtle. An experimenter may accidentally manipulate a variable in addition to the variable of interest.