The remainder of the paper is divided into four sections, dealing respectively with: the standard statistical approach to attribute sampling, consonant belief functions and their relationship with statistical evidence, the belief-function approach to attribute sampling, and finally a summary and conclusion with potential research problems.
Usually in attribute sampling, we test whether the occurrence rate, P, of an attribute in the population is equal to a certain value Po or different.
TABLE 1 Sample Size for Attribute Sampling Using Binomial Distribution for a Desired Belief in the Interval B = [0, TER] with TER = 0.
We consider an auditing example here to illustrate the process of determining the sample size for an attribute sampling using belief functions.
TABLE 2 Sample Size for Attribute Sampling Using Binomial Distribution for a Desired Belief in the Interval B = [0, TER] with TER = 0.
We have demonstrated in this article how beliefs can be assessed from the statistical evidence based on attribute sampling.
Although based on attribute sampling
principles, the objective of MUS (estimating the monetary misstatement in the population) is quite different from the objective of "traditional" attribute sampling
(estimating the population misstatement rate).