What are the myths associated with linear regression, data transformations? I have been encountering many assumptions associated with linear regression (especially ordinary least squares regression) which are untrue or unnecessary. For example:

*

*independent variables must have a Gaussian distribution

*outliers are the points either above or below the upper or lower whiskers correspondingly (employing Boxplot terminology)

*and that the sole purpose of transformations is to bring a distribution close to normal in order to suit the model.

I would like to know what are the myths that are generally taken for facts/assumptions about linear regression, especially concerning associated nonlinear transformations and distributional assumptions.  How did such myths come about?

Where do these ideas come from?
Poor texts (correction: very poor texts) after treating descriptive statistics often include some more or less mangled version of an idea that (1) you ideally need normally distributed variables to do anything inferential, or else (2) you need non-parametric tests. Then they may or may not mention that transformations could get you nearer (1).
The first context for writing like ,this is often Student $t$ tests for comparing means and (Pearson) correlations. There is some historical context for this, for example in treatments that focused on a reference case of a bivariate normal distribution with a correlation as one parameter.
So then writers start talking about regression.
These texts are usually innocent of any formal specification of a data generation process.
