The error term contains the influence of many different forces (random variables) that affect your dependent variable (Y) and aren’t captured by your independent variables (Xs). The central limit theorem indicates that the sum or mean of random variables is normally distributed as
long as many random variables are present and the influence of any one random variable is small (check out Chapter 3 if you need to review the central limit theorem and its implications).