I am trying to calculate the sample size required to assess a process improvement that is expected to reduce the proportion of high values of a final measurement by 15% to 30%. I would like to know the number of samples required such that the process improvement compared to historical results is detected with various levels of confidence (e.g., 90%, 95%, etc.). The historical data distribution I am working with is not continuous data (ordinal) and is not normally distributed. A distribution showing sample data is attached.
Can anyone help me with this? Thanks.
If you have a known historical distribution I suggest doing it via a simulation. Unfortunately, I have no idea of how to do that in JMP
One approach would be to treat the data as binomial, high and not high. Then you're looking for a binomial sample size which should be moderately easy, but like Reeza I don't know how to do that in JMP.