How do we handle zero defects or non-conformities when sampling a finite population of objects?
A. In this the third article on the topic of zero defects, we discuss the scenario wherein sampling is done from a finite lot of objects. In the context of acceptance sampling, this is referred to as sampling an isolated lot. We denote the lot size using N and the sample size using n. Sampling is done randomly and without replacement. Let D be the unknown possible number of non-conforming objects originally in the lot. When the sample has been collected and we find x = 0 non-conforming objects, the question is: What is the largest value that D could be with some stated confidence C? This upper bound value is designated Du. Having found this value, we then may state that D ≤ Du at confidence level C. Then Du is the upper 100C% confidence bound for the unknown D. We may also have a consumer’s risk of, say, β. This means that if a non-conforming object is inspected, there is a probability equal to β that the object will be incorrectly classified as conforming and hence a risk to the consu
