Hi Statistical Seven,

I'm sorry for not cleary getting to the point. Yes, I'm looking for some acceptance sampling plans or methods.

What do I have to apply, if I have an AQL=0,01 to fullfil and the data of the measured variable is not normal distributed - and can not be transformed into normal data. And the lot size is about 100k to 150k.

e.g destructive Dial Torque meaurements, no target value, only upper and lower limits.

LSL 10mNm

USL 95mNm

Waeller

Waeller

Here are several options, not are better than others, but just how I approach a problem like this.

First I try to under why my data is not normal for a two-sided specification of torque. I expect non-normality with a one-sided specification, but usually do not see normality unless it's bi-modal. If the data is in fact bi-modal, then acceptance testing is a complex issue as you need to know the distribution of the parts from each mode. Without a fundamental understanding of the data, using continuous (either normality or non-normality) acceptance is an exercise in futility.

My usual response is that though we measure torque as a continuous value, we can use attribute based sampling as the parts either pass or don't pass. Again, without a distributional assumption about the data, cannot calculate the mean and standard deviation to predict percent out of specification.

You can use reliability type models to predict probability of exceeding the specification using different sample sizes based on exponential or weibull models. This is NOT a perfect solution, and requires an iterative process for finding the best solution.

Assume normality and use 3951. This is ONLY used if the lack of normality is small and the cause is from "outliers".

Otherwise, I am stumped!