[xadhominemx]

That is incorrect. Least squares follows directly from the central limit theorem.

[jampekka]

Central limit theorem tells in practice that gaussian distributions is can be expected to be quite common. And it makes the gaussian distribution a good first guess. Least squares gives the ML estimate for gaussian residuals. I don't find this very direct, and there being a rationale doesn't mean that rationale is what in reality drives the usage.

I mention the relation to the gaussian distribution. Which part of the comment is incorrect?

[xadhominemx]

This part is incorrect: “ The main practical reason why square error is minimized in ordinary linear regression is that it has an analytical solution”

OLS is popular because it gives correct answers as a result of the CLT

[disgruntledphd2]

And it has an analytical solution, which was important before computing (and still makes it quicker today).

[kyllo]

In other words, as economists say, because OLS is provably the BLUE (Best Linear Unbiased Estimator) aka the Gauss-Markov Theorem.