To show robustness of a chemical process, we often need to show regulators that selected parameters (temp, equivalents, time and reagent ratios) are not impacting the quality (purity) of final product. In order to show this, regulators prefer performing screening designs. Usually, we want to show that the parameters do not have detrimental effect within the ranges studied, so that the process is robust.
My question is if the experimenter performs with utmost precision, and the data shows that none of the factors (or interaction terms) are influencing quality, then that's fine. If the experimenter collected data that is not good and the model shows that none of the factors are influential, then how to figure out if we can trust the data and the model? Is there anyway, especially if we don't have replicate runs to see variation?
I see that Definitive Screening Designs do not necessarily include replicate experiments, then how to know if there is significant variability in the responses?
