Several new methods of evaluatIng dollar-unit samples have been proposed recently for use in auditing. This paper presents results of a simulation study that compared four of these new methods. Additional simulations on alternative forms of two of these are also presented. Evidence on the reliability and relative size of the upper error bounds for each method is provided for 2,160 different combinations of sample sizes, confidence levels, and types of accounting populations. The results provide a comprehensive analysis of the capabilities of these methods. All four methods have strengths and weaknesses. The Multinomial Dirichlet bound is quite reliable but very conservative and (as formulated) not able to utilize understatement errors. The two versions of the Cox and Snell bound that were tested (with different priors) varied considerably in their reliability over a range of confidence levels. While not without an occasional reliability problem, test results for the Moment Method bound and one of the two Bayesian-Normal bounds were largely favorable.

[Several new methods of evaluating dollar-unit samples have been proposed recently for use in auditing. This paper presents results of a simulation study that compared four of these new methods. Additional simulations on alternative forms of two of these are also presented. Evidence on the reliability and relative size of the upper error bounds for each method is provided for 2,160 different combinations of sample sizes, confidence levels, and types of accounting populations. The results provide a comprehensive analysis of the capabilities of these methods. All four methods have strengths and weaknesses. The Multinomial Dirichlet bound is quite reliable but very conservative and (as formulated) not able to utilize understatement errors. The two versions of the Cox and Snell bound that were tested (with different priors) varied considerably in their reliability over a range of confidence levels. While not without an occasional reliability problem, test results for the Moment Method bound and one of the two Bayesian-Normal bounds were largely favorable.]

[When making audit judgments and decisions, an auditor often relies on knowledge or information about how task variables covary. Typically, an auditor generates this information without the aid of a formal covariation model. Thus, the quality of audit judgments and decisions which rely on covariation information may depend on an auditor's ability to judge covariation in a manner that is paramorphic to a formal model. This paper reports two experiments examining the rules by which auditors integrate joint frequency data when making covariation judgments and whether their judgments are affected by context, prior expectations, and amount of auditing experience. A main result was that the subjects generally used data-integration rules that were sensitive to the objective covariation level, but often overstated or understated this level. The effects of context, prior expectations, and amount of auditing experience were generally small.]

When making audit judgments end decisions, an auditor often relies on knowledge or information about how task variables covary. Typically, an auditor generates this information without the aid of a formal covariation model. Thus, the quality of audit judgments and decisions which rely on covariation information may depend on an auditor's ability to judge covariation in a manner that is paramorphic to 8 formal model. This paper reports two experiments examining the rules by which auditors Integrate joint frequency data when making covariation judgments end whether their judgments are affected by context, prior expectations, and amount of auditing experience. A main result was that the subjects generally used date-integration rules that were sensitive to the objective covariation level, but often overstated or understated this level. The effects of context, prior expectations, end amount of auditing experience were generally small.