> Fractional factorial is also most effective when the covariates are orthogonal and independent, which is rarely the case. We can get around this by projecting

> Fractional factorial is also most effective when the covariates are orthogonal and independent, which is rarely the case. We can get around this by projecting high dimensional covariates into lower dimensional space using PCA, which guarantees orthogonality, but this also seems to not be done so much. I guess you're right in that if there were very many interactions between variables, then the aliasing of a fractional design would be an incredible nuisance, but on the other hand if there were no dependence between any of the variables at all then there would be no point to any sort of factorial design as you could just test each variable sequentially. > Just wondering if anyone is using fractional factorial designs in real life? (or optimal designs like D-optimal designs) I have to admit, I have never ever seen anyone calculate a D-optimal or G-optimal design for anything -- that said, I only graduated as a statistician 6 or 7 years ago. As you say, there might be some use to it for continuous variables but for factors, given that latin squares (etc.) are known to be optimal so you can just grab that off the shelf.