I have a question (simpler than my previous post today I hope!), which is probably very stupid as nobody has never asked it before.
Lets say I am trying to explain the effect of 3 variables (A, B and C) on a dependent one (Y). Biologically speaking, A and B should really have an effect on Y. So I am testing:
Y ~ A + B + C
But when I use a model selection method (whatever the method is), the 'best' model, the one that fits the data the best, drops A. So I end up with:
Y ~ B + C
What can I say about A then?
Can I cite something to justify the dropping (the F statistics, something about the AIC/BIC, etc)?
If I need to show that A has no effect, do I need to use the full model anyway?