I am currently trying to design an experiment that will be testing how fast a conveyor is able to convey different size cases. The case sizes are based on 4 dimensions (LxWxH and Weight) and the case population size is well over 100,000 combinations.
I am looking for some insight on what kind of sample size I need to test to be able to be 95% confident that I have accounted for the 100,000 possible combinations. What kind of statistical analysis can I run on the population data that can tell me the how many and what size cases I need to test in my experiment that will represent the overall case population?
Just looking for some direction so that I can do further research on my own.
EDIT: I apologise for the delay in answering your questions.Experimentation is not expensive but is time constrained. I have 120 days to experiment and can most likely achieve 1 experiment run per day and therefore get 120 data points. The data is continuous and normal around a central mean I heave measurements on all four factors I agree that a full factorial design is applicable and is the approach I would like to take. Kjetil, I believe you are saying that I can use the population data I have to break the factors into a HIGH/LOW category by looking at their distribution and choosing percentage values accordingly?
A little more background on the experiment is that we are unloading these cases from a trailer and when we experiment we are only able to measure an unload rate (Cases / Hour) for the entire trailer and not by each type of case. My thought is that I can take the case data that I have and break the cases into groups based on the four factors (example - Small, Medium, Large) and those would be my experiment factors. My levels would then be the percentage % of the each of those categories that we unload from the trailer (i.e we unload a trailer that is 25% Small, 50% Medium, 25% Large).