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?

Linear regression, like least square regression, does not assume Gaussian distributed error terms.
The assumption of Gaussian distributed error terms relates to statistics like hypothesis tests or computations of confidence intervals. For these types of statistics, it is necessary to have more precise assumptions about the hypothetical distribution of the error. And even for these types of statistics the assumption of Gaussian distribution is not really necessary. What we need is that it is approximately Gaussian distributed (Which statistical analysis should I perform if the data sets are not normally distributed?).
