Neyman-Pearson and Bayes Interval Estimates for Sampling by Atiributes

Confidence intervals can provide important information about statistical estimates of EMP protection parameters during test programs which are often constrained by budgetary or other considerations. Under some circumstances Bayesian interval estimates are contained within Neyman-Pearson estimates. For example, this is true in the single stage case with uniform prior distribution under the constraint of equation (15). Differences between the Neynlan-Pearson and Bayesian intervals (with uniform prior distribution) rapidly decrease as the sample size increases. For sample sizes in excess of roughly 40 these differences are usually of little practical importance. Multistage sampling is usually employed to reduce the experimental program with a potential penalty in the width of the confidence interval. As demonstrated above, two stage confidence intervals contain corresponding intervals computed from a fixed sample size when the outcome is near acceptance-rejection boundaries and the level of confidence exceeds about 90%. When all or nearly all samples fail at some later stage, confidence bounds computed from multistage sampling are noticeably different from those computed with fixed sample size. In this circumstance the mathematical decision is to reject the sample. However, the mathematical decision is often only part of the overall operational decision. This mathematically unlikely occurrence can serve as a warning that unanticipated change or error has occurred during the test. Confidence limits computed from multistage sampling must then be employed with the usual caution which surrounds an unanticipated result.