Why is it considered essential to account for error assumptions in linear regression, and what does it imply?

Question

Anonymous · Accepted Answer

An error in linear regression is normally distributed. This is to ensure valid
statistical inference of the computed coefficients and for hypothesis testing.
 Intuitively, we want to make sure the error is normally distributed to
compensate for both underfitting and overfitting

Anonymous · Answer

Linear regression assumes that the noise is gaussian. This would cause error in
the approximation if the noise added was not from a symmetric distribution. For
example, if the error was drawn from a poisson distribution, then the tail
events would overly influence the fit.

Why is it considered essential to account for error assumptions in linear regression, and what does it imply?

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