The central limit theorem tells us that the distribution of the sum of independent random variables approaches the normal distribution as the number in the sum becomes large, The theorem applies for almost any starting distributions with finite variances. If we think of the error term as the sum of all the many factors excluded from our model and, furthermore, we believe that these excluded factors are not systematically related to one another or to the included variables, then the central limit theorem suggests that the distribution of the error terms will be approximately normal.