My question is regarding linear regression and non-constant variance. I've heard that even though a large data set fails a normality test, this does not necessarily mean the data is not normal. This is because the tests for normality can be 'overly' sensitive.' Thus, I've heard that, in practice, people overlook when normality fails.
Are the tests for constant variance like that? Or should I be able to keep transforming the data until it passes tests for constant variance? Also, I read that the constant variance tests like the breusch pagan test are sensitive to normality. If my data set fails the normality test, then will BP also fail?
My data set is over 2500 observations and has many predictor variables. When I take a look at the scatter plots, they look reasonable.