Audit Sampling 1 Flashcards
What is Audit Sampling?
Refers to the examination of less than 100% of a population and using the results as a basis for drawing a general conclusion on the entire population
What are the characteristics of Statistical Sampling?
- Statistical Sampling refers to the use of quantitative measures of the risks the auditor is taking in the use of sampling.
- Based on formulas
- Statistical sampling helps the auditor to: (1) design an efficient sample, (2) measure the sufficiency of the audit evidence obtained, and (3) evaluate the sample results.
- Helps the auditor to design an efficient sample, measure the sufficiency of the audit evidence obtained, and evaluate sample result → An advantage of statistical sampling over nonstatistical sampling
What are the characteristics of Non-Statistical Sampling?
- Based on human decision - lack mathematical certainty - judgmental sampling
- Equally acceptable as Statistical Sampling
- Still be effective but not efficient
When Audit sampling is used during the Audit
- During Internal Control phase of the audit - auditor will perform test of controls on a sample to determine the operating effectiveness of controls they plan to rely on - Attribute sampling
- During the substantive testing phase of the audit, the auditor will perform test of details of transactions, accounts and disclosures on a sample basis to obtain sufficient appropriate audit evidence to support management assertions - Variables sampling
- Both Attribute sampling and Variable sampling may be either statistical or nonstatistical
-
Dual-purpose tests are those in which a single sample is used to test a control and to serve as a substantive test
of a recorded balance or class of transactions. When a dual-purpose test is used, auditors select the sample size
as the higher of that required for the two purposes. For example, if the test of control test required thirty-five
items, and the substantive test required forty, both tests would be performed using the forty items.
Types of Statistical Sampling Plans
a. Attributes sampling (used in tests of controls) reaches a conclusion in terms of a rate of occurrence—
b. Variables sampling (used in substantive testing) reaches a conclusion in dollar amounts (or possibly in units).
(1) Probability-proportional-to-size (PPS) sampling
(2) Classical variables sampling techniques
What are the characteristics of Variables sampling?
- Generally used for substantive testimate
- Estimate value of population based on value of items in sample
- Determine whether or not estimated value is close enough management’s assertion as to valuation
- Testing for a dollar amount
Attribute Sampling
- Attribute sampling is used for test of controls
- Used to reach a conclusion about a population in term of rate of occurence
- Determine whether or not estimate error rate indicates control is working effectively
- Deals with deviation rate - Errors are stated in terms of %- not dollar amounts
- Attributes sampling is generally used when there is a trail of documentary evidence
- Attribute sampling is based on the binomial distribution which describes yes/no decisions, and error/nonerror situations.
How does Sampling Risk compare to Non-Sampling Risk?
-
Nonsampling risk includes all aspects of audit risk that are not due to sampling. It is controlled by adequate planning and supervision of audit work and proper adherence to quality control standards. risk of “human” type errors (e.g., failure to detect a misstatement).
Types The followingare examples of nonsampling risk: (a) The failure to select appropriate audit procedures (b) The failure to recognize misstatements in documents examined (c) Misinterpreting the results of audit tests - Sampling risk is the risk that the auditor’s conclusion, based on a sample, might be different from the conclusion that would be reached if the test were applied in the same way to the entire population (AU-C 530).
Sample risk and test of control
What is the risk of assessing Control Risk too high?
- Type I
- Population is okay but the Sample is bad
- Under-rely on Internal Control
- Assess RRM ↑
- More substantive tests expanded beyond the necessary level- Sample overstates Control Risk-
- Leads to an - overall audit inefficiency
- Audit ends up being effective (correct result) - but you do more work
Sample risk and test of control
What is the risk of assessing Control Risk too low?
- Type II Error
- Population is bad but the Sample is OK
- Over-rely on Internal Control
- Assess RRM ↓
- Less substantive tests -
- Leads to an - less effective
- Audit ends up being ineffective incorrect result – greater concern
Sample risk and substantive test
What is the risk of Incorrect Acceptance?
A risk of Substantive Testing - Auditor accepts a balance as fairly stated- when in fact it is not fairly stated - Hurts audit effectiveness
- Wrong conclusion reached
- related to audit effectiveness
- Type II Error / Beta
Sample risk and substantive test
What is the risk of Incorrect Rejection?
A risk of Substantive Testing - Auditor rejects balance as not fairly stated when in fact it is fairly stated - Hurts audit efficiency
- Wrong recommendations given
- audit efficiency
- Type I Error
Planning Attribute sampling
— Determine the sample size —
In planning for attribute sampling, the auditor should
1) Determine Allowable risk of overreliance or sampling risk (assessing control risk too low)
2) Establish Tolerable deviation rate
3) Determine the Expected population deviation rate
4) Calculate the Sample Size
How do you determine if Control Procedures are operating properly or not operating properly?
Control Procedures are either operating properly or they are not operating properly - based on Error Rate and the tolerance you have for errors
What is the Tolerable Rate (tolerable deviation rate)?
- Error rate in population that you are willing to accept/tolerate without modifying the planned level of control risk (RMM)
- The auditor’s determination of the tolerable deviation rate is a function of
a] The planned assessed level of control risk.
b] The degree of assurance desired by the sample. - Inverse relationship to Sample Size
- Higher Tolerable Rate = Smaller Sample size
- Lower Tolerable Rate = Larger Sample size
- If you’re willing to accept a higher probability that errors exist- there is less pressure on the sample
