Glen_b has explained nicely that OLS regression can be generalized (maximizing likelihood instead of minimizing sum of squares) and we *do* choose other distributions. However, why is the normal distribution chosen so *often*? The reason is that the normal distribution occurs in many places naturally. It is a bit the same like we often see the golden ratio or the Fibonacci numbers occurring "spontaneously" at various places in nature. The normal distribution is the limiting distribution for a sum of variables with finite variance (or less strict restrictions are possible as well). And, without taking the limit, it is also a good approximation for a sum of a finite number of variables. So, because many observed errors occur as a sum of many little unobserved errors, the normal distribution is a good approximation. See also here https://stats.stackexchange.com/questions/49212/importance-of-normal-distribution where Galton's bean machines show the principle intuitively 