I have a powder batch A from which I am drawing two samples and feeding into two different methods (A and B) of a process to determine if both methods produce the same output. However, I realize since the powder has a particle size distribution, it would mean the samples have a subset of that distribution and these sampler powders are the input for methods A and B. So when I compare the output of method A and method B, how do I take into account the probable different in distribution of the inputs? Do I take the batch A PSD mean and std deviation and somehow adjust the output powder PSD mean and std deviation with those values?
I have a powder batch (Batch A) with a known particle size distribution (PSD). From this batch, I draw two separate samples to feed into two different methods (Method A and Method B) of a process. I want to compare the output of both methods to see if they produce the powder output PSD result.
However, since the input powder batch A has a particle size distribution (range of particle sizes), each sample I take will only represent a subset of the overall distribution, which might affect the output results of both methods. Given this, how should I account for the differences in particle size between the samples when comparing the outputs of Methods A and B?
Should I adjust the results by considering the overall PSD of the batch (i.e., the mean and standard deviation of the particle size distribution) and somehow factor those into the output PSDs of the two methods?
