I am coming from the field of psychology and in most publications Model Selection (OLS, Regression) is done via Forward/Backward Selection using the F-Static/p-value of the regression coefficients to decide which coefficient to include/exclude in the next step.
Now i read in the statistical learning literature (for example in ESL) that this strategie is outdatet because "it doesn't account for multiple testing issues correctly".
Unluckily there is no further explanation on those problems.
Does anybody can explain me, what exactly is the problem of this approach and why using other criteria (Cp, AIC) is better?
Thanks a lot in advance!