Hi @Students_Tea,
Welcome in the Community !
When creating a design using blocking like you did, you group experimental runs into blocks that are similar to each other. The repartition of factors levels should be similar between blocks 1 and 2, so if you can't run experiments of block 2, the situation is similar to using a fraction of the full design. Using only half of the experiments planned in this situation will however results in :
- High reduction of power to detect significant main effects and higher order effects:
- Higher prediction variance :
- Higher uncertainty for parameters estimates :
- Increase correlations between effects :
So you may already have detected a significant effect for factor A, both statistically and with a large effect size, but since you're degrading the ability to detect effects (and particularly higher order effects in which you seem to be interested) when using only the first block of your complete design, I would recommend running the other planned experiments from block 2 to better estimate effect from factor A and give you the ability to detect other effects (main effects and higher order effects).
You could also augment the design and changing the ranges of the factors : if A has a predominant effect on your response, you could augment your initial set of experiments from block 1 and restrict the levels/range of A to narrower values centered around the intermediate level (if you had good results in this area). This could help reduce the relative importance of factor A on the response. You could also expand ranges from factor 2, if it makes sense, to enable an easier detection of an effect.
I wouldn't fix a factor at this stage, you have a limited number of factors to study and very low power to detect effect when using only a fraction of your complete design. Fixing a factor so early in the experimentation stages could prevent you from detecting possible interaction between A and B, as well as a more precise estimation of possible quadratic effect of A and its main effect. Maybe the detection of interactions and quadratic effects (not yet detected at this stage) could lead you to a better optimum and a better overview and understanding of your system.
I attached a design similar to the one you seem to have so that you can reproduce the design comparison (full and subset with block 1 only) with the Compare Designs platform.
Hope this answer may help you,
Victor GUILLER
Scientific Expertise Engineer
L'Oréal - Data & Analytics