![]() What if you don’t know in advance of testing that the data is going to non normal? Our protocols usually call for 30 samples for variable data, do you see any problem with going back to the original pool of test samples and testing an additional 30 or more samples to establish normality or at least increase the achieved confidence level? 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. For example a Weibull or lognormal distribution. Someone suggested we try and find a more suitable distribution that best fits the data. Does anyone have any thoughts on this?Ģ.) When the data is found to be non normal, I usually just use the nonparametric value that Minitab calculates for you. 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. ![]() We test the 30 samples and calculate tolerance intervals, using Minitab, on the results. 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. 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. The confidence level can be increased if additional samples are taken from the sample pool. I have two questions on using Minitab to calculate tolerance intervals.ġ.) 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). ![]() Apologies if there is already a thread which discusses Minitab and tolerance intervals, I couldn’t find it. ![]()
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