Chapter 11 Audit sampling Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Auditing and Assurance Services in Australia 3r by Grant Gay and Roger Simnett Slides prepared by Roger Simnett

Learning objective 1: Definition and features Audit sampling: the application of an audit procedure to less than 100% of the items within a population to obtain audit evidence about particular characteristics of the population (ASA 530.06/ISA 530.03). Audit sampling is important because it provides information on: How many items to examine Which items to select How sample results are evaluated and extrapolated to the population in order to tell us something about the population (e.g. level of misstatement) Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Auditing and Assurance Services in Australia 3r by Grant Gay and Roger Simnett Slides prepared by Roger Simnett

Sampling risk defined Sampling risk: the probability that the auditor has reached an incorrect conclusion because audit sampling was used rather than 100% examination (i.e. correctly chosen sample was not representative of the population). Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Auditing and Assurance Services in Australia 3r by Grant Gay and Roger Simnett Slides prepared by Roger Simnett

Non-sampling risk defined Non-sampling risk: arises from factors, other than sample size, that cause an auditor to reach an incorrect conclusion, such as the possiblility that: The auditor will fail to recognise misstatements included in examined items The auditor will therefore apply a procedure that is not effective in achieving a specific objective. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Auditing and Assurance Services in Australia 3r by Grant Gay and Roger Simnett Slides prepared by Roger Simnett

Characteristic of interest When sampling, the auditor identifies a particular characteristic of the population to focus upon. For tests of control, the characteristic of interest is the rate of deviation from an internal control policy or procedure. For substantive tests, the characteristic of interest is monetary misstatement in the balance. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Auditing and Assurance Services in Australia 3r by Grant Gay and Roger Simnett Slides prepared by Roger Simnett

Learning objective 2: Various means of gathering audit evidence 100% examination: this is not a sampling method Selecting specific items: e.g. high value or high risk — this is not a sampling method. Items selected will not necessarily be representative of the population Audit sampling Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Auditing and Assurance Services in Australia 3r by Grant Gay and Roger Simnett Slides prepared by Roger Simnett

Statistical sampling defined Statistical sampling: any approach to sampling that has the following characteristics: Random sample selection Use of probability theory to evaluate sample results, including measurement of sampling risk. Major advantage of statistical sampling over non-statistical sampling methods is defensibility, thorough quantification of sampling risk. Refer ASA 530.13 (ISA 530.10). Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Auditing and Assurance Services in Australia 3r by Grant Gay and Roger Simnett Slides prepared by Roger Simnett

Non-statistical sampling Non-statistical sampling: sampling approaches that do not have all the characteristics of statistical sampling. Major advantage of non-statistical sampling is greater application of audit experience. The basic principles and essential procedures identified in ASA 530 (ISA 530) apply equally to both statistical and non-statistical sampling. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Auditing and Assurance Services in Australia 3r by Grant Gay and Roger Simnett Slides prepared by Roger Simnett

Learning objective 3: Planning and designing the sample Auditor must consider: Objectives of the audit test (usually related to an audit assertion of interest) Population from which to sample Possible use of stratification Definition of the sampling unit. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Auditing and Assurance Services in Australia 3r by Grant Gay and Roger Simnett Slides prepared by Roger Simnett

Defining the audit objective and population Once the audit objective is specified, such as reliance on controls or misstatement of account balance, the auditor must consider what conditions would constitute an error. The auditor must ensure that the population from which the sample is to be selected is complete and appropriate to the audit objective. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Auditing and Assurance Services in Australia 3r by Grant Gay and Roger Simnett Slides prepared by Roger Simnett

Stratification Stratification: occurs when the auditor divides the population into a series of sub-populations, each of which has an identifying characteristic, such as dollar value. Can assist with audit efficiency as it allows the auditor to reduce the sample size by reducing variability, without increasing the sampling risk. Can direct auditor’s attention to areas of audit interest, especially risky or material items. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Auditing and Assurance Services in Australia 3r by Grant Gay and Roger Simnett Slides prepared by Roger Simnett

Defining the sampling unit Sampling unit is commonly the: Transactions or balances making up the account balance; or Individual dollars that make up an account balance or class of transactions, commonly referred to as Probability Proportionate to Size Sampling (PPS) or Dollar Unit Sampling (DUS). Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Auditing and Assurance Services in Australia 3r by Grant Gay and Roger Simnett Slides prepared by Roger Simnett

Learning objective 4: Determining sample size Sample size is affected by the degree of sampling risk the auditor is willing to accept. Auditor's major consideration in determining sample size is whether, given expected results from examining sample, sampling risk will be reduced to an acceptably low level. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Auditing and Assurance Services in Australia 3r by Grant Gay and Roger Simnett Slides prepared by Roger Simnett

Factors that influence sample size for tests of controls Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Auditing and Assurance Services in Australia 3r by Grant Gay and Roger Simnett Slides prepared by Roger Simnett

Factors that influence sample size for substantive testing Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Auditing and Assurance Services in Australia 3r by Grant Gay and Roger Simnett Slides prepared by Roger Simnett

Learning objective 5: Selecting the sample To draw conclusions about population or strata, the sample needs to be typical of characteristics of population or strata. Sample needs to be selected without bias so that all sampling units in the population or strata have a chance of selection. Common sampling techniques are: Random selection — random number generation Systematic selection Haphazard selection — select without conscious bias Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Auditing and Assurance Services in Australia 3r by Grant Gay and Roger Simnett Slides prepared by Roger Simnett

Steps in systematic selection For example, suppose the sample size is 20 and the number of items in the population is 10 000: Step 1: Calculate the sample interval: Step 2: Give every item in population chance of selection by choosing a random number (random start) within range of 1 and sampling interval (in this example, 500), e.g. 217. Step 3: Continue to add sampling interval to random start, and identify items to be sampled, e.g. item nos. 217, 717, 1217. . . 9217, 9717. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Auditing and Assurance Services in Australia 3r by Grant Gay and Roger Simnett Slides prepared by Roger Simnett

Learning objective 6: Performing the audit procedures To ensure conclusions arising from tests on audit samples are appropriate, auditor must perform testing on each item selected. If selected item is not appropriate for application of testing procedure, a replacement item can be selected. If auditor is unable to perform test on a selected item (e.g. loss of documentation), it is considered to be an error. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Auditing and Assurance Services in Australia 3r by Grant Gay and Roger Simnett Slides prepared by Roger Simnett

Evaluating sample results To evaluate sample results, auditor determines the level of error found in sample and directly projects this error to relevant population. For example: sample 20%, find misstatement of $10 000. Therefore projected error = $50 000 ($10 000/20%). Projected error is then compared with tolerable error for the audit procedure to determine if characteristic of interest can be accepted or rejected. Auditor should consider both the nature and cause of any errors identified. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Auditing and Assurance Services in Australia 3r by Grant Gay and Roger Simnett Slides prepared by Roger Simnett

Learning objective 7: Sampling for tests of controls, attribute sampling Audit sampling is useful for tests of controls, especially involving inspection of source documentation for specific attributes such as evidence of authorisation (attribute sampling). Involves examination of documents for particular attributes related to controls (e.g. authorisation). Results of attribute sampling can be used to support or refute an initial assessment of control risk. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Auditing and Assurance Services in Australia 3r by Grant Gay and Roger Simnett Slides prepared by Roger Simnett

Planning and designing sample for tests of controls Auditor should consider: Audit objectives (assertions of audit interest) Tolerable error — maximum error rate that would still support control risk assessment Allowable risk of over-reliance — allowable risk of assessing control risk too low Expected error — amount of error the auditor expects to find in the population Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Auditing and Assurance Services in Australia 3r by Grant Gay and Roger Simnett Slides prepared by Roger Simnett

Reliability factors for assessing required confidence level Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Auditing and Assurance Services in Australia 3r by Grant Gay and Roger Simnett Slides prepared by Roger Simnett

Sample size estimation for attribute sampling Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Auditing and Assurance Services in Australia 3r by Grant Gay and Roger Simnett Slides prepared by Roger Simnett

Sample size estimation for attribute samples (alternative method) An alternative method is to determine sample size by reference to: Table 11.5 (p. 532), for where allowable risk of over- reliance (ARO) is 10% (90% confidence). This ARO is common in practice. Table 11.6 (p. 532), for where allowable risk of over- reliance is 5% (95% confidence). Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Auditing and Assurance Services in Australia 3r by Grant Gay and Roger Simnett Slides prepared by Roger Simnett

Evaluation of attribute sample results Approach in practice is to use sample deviation rate (SDR) as best estimate of population deviation rate. For example, auditor selects 25 items, finds one error => SDR rate is 4%. Auditor compares with tolerable deviation rate (TDR). If SDR <= TDR, sample results support auditor’s planned reliance on IC. If SDR > TDR, sample results do not support auditor’s planned reliance on IC, auditor will revisit audit plan and reduce reliance on IC and increase substantive testing. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Auditing and Assurance Services in Australia 3r by Grant Gay and Roger Simnett Slides prepared by Roger Simnett

Learning objective 8: Sampling for substantive tests The following matters should be considered: Relationship of sample to relevant audit objective (assertion of audit interest) Preliminary judgments about materiality levels Auditor's allowable risk of incorrect acceptance Characteristics of the population Use of other substantive procedures directed at same financial report assertion. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Auditing and Assurance Services in Australia 3r by Grant Gay and Roger Simnett Slides prepared by Roger Simnett

Dollar-unit sampling Sample unit is individual dollar units, not physical units (transactions or balances). A population with $1 000 000 that contains 1000 physical units or accounts is viewed as a population with 1 000 000 sample units. Individual dollar selected is attached to that physical unit or account in which it is contained, which will then be tested. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Auditing and Assurance Services in Australia 3r by Grant Gay and Roger Simnett Slides prepared by Roger Simnett

Advantages of dollar-unit sampling (DUS) Directs auditor’s attention to material items. For example, under traditional sampling, debtor A (owing $10 000) and debtor B (owing $1000) have equal chance of selection. Under DUS, debtor A is ten times more likely to be selected and tested. Directs auditor’s attention towards overstatement errors. However, a disadvantage is that it directs auditor’s attention away from understatement errors. Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Auditing and Assurance Services in Australia 3r by Grant Gay and Roger Simnett Slides prepared by Roger Simnett

Determination of sample size for substantive tests For convenience, this is usually presented as: E.g. account balance $1 000 000. Tolerable error $50 000. Expected error is zero and risk of incorrect acceptance is 5%  Reliability factor = 3 (Table 11.4, p. 531) Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Auditing and Assurance Services in Australia 3r by Grant Gay and Roger Simnett Slides prepared by Roger Simnett

Illustration of DUS with systematic selection Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Auditing and Assurance Services in Australia 3r by Grant Gay and Roger Simnett Slides prepared by Roger Simnett

Illustration of DUS with systematic selection (cont.) Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Auditing and Assurance Services in Australia 3r by Grant Gay and Roger Simnett Slides prepared by Roger Simnett

Evaluation of sample results for substantive testing Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Auditing and Assurance Services in Australia 3r by Grant Gay and Roger Simnett Slides prepared by Roger Simnett

Learning objective 9: Other statistical sampling approaches Mean per unit estimation Difference estimation Ratio estimation Copyright  2007 McGraw-Hill Australia Pty Ltd PPTs t/a Auditing and Assurance Services in Australia 3r by Grant Gay and Roger Simnett Slides prepared by Roger Simnett