Sanjay Srivastava makes a point that is often forgotten when researchers refer to error variance:
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.