There are a lot of similar questions here but I have not found an answer to this specific question.
Source: for example in https://peopleanalytics-regression-book.org/linear-reg-ols.html#norm-dist-assum the author (a mathematician) says:
In an appropriate model we expect our errors to be random, so we would therefore expect our residuals to be normally distributed over sufficient numbers of observations.
The author then goes on to apply
qqnorm(newmodel$residuals) to the data for diagnostics.
If you plot a model in R (
plot(mymodel)), you get a bunch of diagnostic plots, the second of which is standardized residuals plotted against the theoretical quantiles - so essentially the same.
But why? What is the reasoning behind the residuals being normally distributed, and not just randomly, without having a recognised distribution at all, or some other distribution? Stats textbooks treat this as if it was obvious - could someone explain, please?