Why we prefer normal distribution of data in linear regression As I recalled, we can also assume the data points come from Laplace distribution and hence it will be the linear regression with absolute error.
Why did so many texts assume the data points came from a normal distribution then?

The main difference with or without normally distributed errors is whether the inference procedures (standard errors, p-values, t-tests etc) hold in small or large samples.
The inference results hold:

*

*If errors are (assumed to be) normally distributed, the normal distribution for the estimates is exact, i.e. it holds even in small samples

*Without the normal assumption, using a normal or student distribution for the estimates is an approximation based on an asymptotic result

