 A common assumption is that the errors belong to a normal distribution.The central limit theorem supports the idea that this is a good approximation in many cases.However, if the errors are not normally distributed, a central limit theorem often nonetheless implies that the parameter estimates will be approximately normally distributed so long as the sample is reasonably large.