George Box, a famous statistician, once said:
“all models are wrong, but some are useful”
And another version:
“Remember that all models are wrong; the practical question is how wrong do they have to be to not be useful”
(Box is among other things known for Box-Cox transformations, the Box-Jenkins models in time series, the book on experimental design Box, Hunter, Hunter and a book on Bayesian inference.)
The ideas above are not only Dasons but are held by many well-known statisticians.
Someone else said:
“The most practical thing is a good theory”.
….
Englund wrote:
“My point is that R^2 is, per difinition, a measure of covariation between the IV´s and the DV”
No, R^2 is not by definition a measure of covariation.
R^2 is a ratio that is based on the fact that you can split up the total sum of squares (SST) in explained sum of squares (SSR) and residual sum of squares (SSE) so that R^2 = SSR/SST.
In my view the R^2 is greatly overemphasized. It is not a “quality index”. It is more dependent on how stretched out the x-values are and the residual variance.