I. CAS 500, 530 summaries of key concepts
II. Introduction to monetary-unit sampling (MUS) also called dollar-unit sampling (DUS) in North America
III. MUS mechanics: Sample Planning
IV. Giant Stores Case
V. MUS Mechanics: Sample Evaluation
VI. Some Statistical Theory
VII. Summary of Required Reading article: Hall et al.
VIII. Solutions to other class questions: EP 1 page 407 SB (10.53), EP 3 page 407 SB (10.55), and DC 1 pages 408-409 SB (10.59)
I. Summary of CAS 500, 530
A. CAS 500: Audit Evidence
This standard is also reviewed in Lecture Notes 5.
CAS 500: audit evidence
Comment: this standard is a review of intro audit concepts including sufficiency and appropriateness of evidence (A1-A6); audit procedures (substantive and tests of controls—A 10-A 25); relevance (A 27- A 30); reliability (A 31-51); and selecting items (A 52-56): 100% exam, selecting specific items, and audit sampling. The standard has a specialized appendix on reliance on actuaries for evidence.
CAS 500 is largely a summary of intro audit concepts. A52 identifies ways of selecting items:
1. Selecting all items (100% exam)
2. Selecting specific items; and
3. Audit sampling
CAS 530 reviews what is involved with audit sampling. The most important for purposes of this class is the definition of key classic sampling concepts in paragraph 5: audit sampling, population, sampling risk, non-sampling risk, statistical sampling, an anomaly to identify unusual misstatements. Also stratification, tolerable misstatements, and tolerable rate of deviation (materiality for tests of controls is called the threshold rate—you can think of this as the amount of control deviations that would lead to a material (tolerable) misstatement. The auditor needs to evaluate all deviations quantitatively and qualitatively.
There are several methods of selecting samples. The first two below are suitable for statistical sampling.
• Random sampling
• Systematic sampling
• Haphazard (judgmental) sampling
• Block sampling
There are 2 key features of statistical sampling:
1. random sampling is selection of items when each item has a predictable chance of selection (this predictable amount is what the formulas are based on and allows prediction of sampling risks). The goal is to obtain a representative sample of the population.
2. statistical evaluation of results (is based on the sampling probability distribution which is based on some probability model using a predictable chance of selection)
There are 2 types of sampling risk:
1. effectiveness risk of CAS 530.05 (c), which we call the beta risk (note this is also referred to as a Type II error risk in your stats course)
2. efficiency risk of CAS 530.05 (c), which we call the alpha risk (note this is also referred to as Type I error risk in your stats course)
CAS 530.14 and CAS 539.A14: auditors must project sample misstatements to the entire population, but this may not be sufficient for deciding on an adjusting entry (specifically, the beta risk may still be too high even for the adjusted amount because the sample size has too high a beta risk associated with it)
CAS 530.A3: tolerable misstatement = overall materiality or performance materiality or lower (we show below that with our formulas you can simply use overall materiality as long as that is sufficient to meet user needs for the account or population in question).
CAS 530.A6: The auditor must clearly define what is a misstatement, both qualitative and quantitative.
CAS 530.A13: Haphazard selection is acceptable but see Hall et al. article on how judgmental biases (these are a form of non-sampling errors) can cause problems. Auditors control non-sampling errors with proper training, supervision, and PA firm quality controls.
CAS 530.A21-A23 (same as AuG-41): decisions can be based on projected...