J

I have two questions on using Minitab to calculate tolerance intervals.

1.) When calculating tolerance intervals using Minitab and the data is found to be non normal, you can use the nonparametric test result; however the associated confidence level is usually lower than the required 95% (we typically looks for 95% confidence). The confidence level can be increased if additional samples are taken from the sample pool.

Is it correct to go back and take additional samples if the desired confidence level is not achieved when using the nonparametric test result? When I say take additional samples, let me clarify what I mean. When carrying out a process validation we might process 500 parts, we would then usually take 30 samples, at random, form this pool of 500 parts. We test the 30 samples and calculate tolerance intervals, using Minitab, on the results.

I personally don’t see why we couldn’t then go back to the pool of 500 parts and take, say an additional 30 samples to add to our Minitab data, if we wanted to increase the confidence level. Does anyone have any thoughts on this?

2.) When the data is found to be non normal, I usually just use the nonparametric value that Minitab calculates for you. Someone suggested we try and find a more suitable distribution that best fits the data. For example a Weibull or lognormal distribution. I have done this for capability analysis, but I don’t see how you can use a different distribution to calculate tolerance intervals using Minitab. Any thoughts?

Thanks,