Acceptance sampling using judgmental and randomly selected samples.
Sego-LH; Wilson-JE; Shulman-SA; Pulsipher-BA; Anderson-KK; Sieber-WK
Washington, DC: U.S. Department of Energy, 2010 Sep; :1-43
We present a Bayesian model for acceptance sampling where the population consists of two groups, each with different levels of risk of containing unacceptable items. Expert opinion, or judgment, may be required to distinguish between the high and low-risk groups. Hence, high-risk items are likely to be identified (and sampled) using expert judgment, while the remaining low-risk items are sampled randomly. We focus on the situation where all observed samples must be acceptable, where the objective of the statistical inference is to quantify the probability that a large percentage of the unsampled items in the population are also acceptable. We demonstrate that traditional (frequentist) acceptance sampling and simpler Bayesian formulations of the problem are essentially special cases of the proposed model. We explore the properties of the model in detail, and discuss the conditions necessary to ensure that the required sample size is a non-decreasing function of the population size. The methodology is applicable to a variety of acceptance sampling problems, including environmental sampling where the objective is to demonstrate the safety of reoccupying a remediated facility that has been contaminated with a lethal agent.
Sampling; Environmental-factors; Occupational-exposure; Risk-factors; Models; Statistical-analysis; Sampling-methods; Surveillance-programs;
Author Keywords: Compliance sampling; Upper tolerance limit; Judgmental sampling; Environmental sampling
Book or book chapter
Acceptance Sampling Using Judgmental and Randomly Selected Samples
Pacific Northwest National Laboratory