AFAIK using the analytic solution for linear regression (via lm in R, statsmodels in python or any other classical statistical package) is still the norm in tra

AFAIK using the analytic solution for linear regression (via lm in R, statsmodels in python or any other classical statistical package) is still the norm in traditional disciplines such as social (economics, psychology, sociology) and physical (bio/chemistry) sciences. I think that as a field, Machine Learning is the exception rather than the norm, where people people start off or proceed rapidly to non-linear models, huge datasets and (stochastic) gradient based solvers. Gaussianity of errors is more of a post-hoc justification (which is often not even tested) for fitting with OLS.