I was reading up on Gerber & Green for during the holiday, and I saw a lot of cautionary warnings about mis-analyzing blocked designs. I think the main concerns are when the treatment assignment is correlated with the outcome (pg 116) (as it should be!) and when the probability of assignment varies by block (pg 76).
One thing I saw highly emphasized in the text is that blocking is particularly useful in small sample sizes because it requires less covariate adjusting. However in other designs, I usually see the estimators using
fixed_effects = blocks which seems to subvert some of the usefulness of blocking in the first place.
As an experimenter, is this something I should be worried about when using small sample sizes? or is there a greater point I’m missing?
Is there another built in way in DD to I should consider evaluating blocked designs (perhaps
weights argument?) If so, what’s best way to do this for a simple design.