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 magic word that nobody seems to have mentioned in this thread is efficiency: we know that if the residuals in a linear regression model $ y = X \beta + \varepsilon $ are normally distributed then the usual OLS estimate of $ \hat \beta = (X^T X)^{-1} X^T y $ is efficient (because it's a maximum likelihood estimator).
The OLS estimator still "works" in the sense of being consistent if $ \varepsilon $ is not normally distributed as long as strict exogeneity is satisfied, i.e. as long as $ \mathbb E[\varepsilon \vert X] = 0 $, since
$$ \mathbb E[\hat \beta] = \mathbb E[(X^T X)^{-1} X^T X \beta + (X^T X)^{-1} X^T \varepsilon] = \beta + \mathbb E[ \mathbb E[(X^T X)^{-1} X^T \varepsilon \vert X]] $$
$$ = \beta + \mathbb E[ (X^T X)^{-1} X^T \mathbb E[ \varepsilon \vert X]] = \beta $$
So all you need to use OLS safely is strict exogeneity, but OLS only makes the most efficient use of the data available if $ \varepsilon $ is normally distributed.
