Collinearity, confidence intervals and sampling

Ok I've been doing some more reseach on this whole collinearity thing and read that if you have collinear variables, the best fitting plane of the data points in a regression will be narrower and less achored (because the predictors are highly correlated so the predictor values fall in a straight line). Consequently, if response varied from sample to sample, the coefficients could change substantially. Therefore the standard errors of the coefficients are necessarily larger.

Does this mean that this is not a problem if you have population level data (ie sample size doesn't matter because you have 'sampled' the entire population you are interested)?

Are there are other effects of collinearity that do not matter if you have population level data? What about other assumptions of regression e.g. normal distribution of variables, homoskedasticity.

The website I've been looking at is http://www.stat.psu.edu/~jglenn/stat501/12multicollinearity/04multico_corr.html which is an excellent source on collinearity.

As always, any replies well appreciated.


.