I often hear researchers criticize each other for treating important phenomena as error variance. For example, situationist social psychologists criticize trait researchers for treating situations as error variance, and vice versa. (And us interactionists get peeved at both.) The implication is that if you treat something as error variance, you are dismissing it as unimportant. And that’s often how the term is used. For example, during discussions of randomized experiments, students who are learning how experiments work will often wonder whether pre-existing individual differences could have affected the outcomes. A typical response is, “Oh, that couldn’t have driven the effects because of randomization. If there are any individual differences, they go into the error variance.” And therefore they get excluded from the explanation of the phenomenon.
I think we’d all be better off if we remembered that the word “error” refers to an error of a model or theory. On the first day of my grad school regression course, Chick Judd wrote on the board: “DATA = MODEL + ERROR”. A short while later he wrote “ERROR = DATA – MODEL.” Error is data that your model cannot explain. Its existence is a sign of the incompleteness of your model. Its ubiquity should be a constant reminder to all scientists to stay humble and open-minded.