ATC F8 materials FF8 AA (Int)Session19 j08

OVERVIEW

Objective

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To identify and describe audit sampling and other selective testing procedures.

GATHERING AUDIT EVIDENCE

AUDIT SAMPLING ALL ITEMS

(100%)

¾ Basic principles

Selection methods

¾ Essential procedure DESIGN

SPECIFIC ITEMS

STATISTICAL v NON-STATISTICAL ¾ Statistical

¾ Non-statistical

¾ Basic principle

¾ Sampling plan

¾ Sample size

¾ Judgmental selection ¾ Definitions

¾ Application

SELECTION

¾ Basic principle

¾ Common methods

¾ Other methods

TESTING

SAMPLE RESULTS ¾ Basic principle

¾ Error projection

1

GATHERING AUDIT EVIDENCE

1.1

Basic principles

1.1.1

Selection methods

Items should be selected for testing by appropriate means.

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Any one or a combination of

‰ selecting all items (100% examination) ‰ selecting specific items

‰ audit sampling.

1.1.2

Risk considerations

Professional judgment should be used to:

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assess audit risk; and

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design audit procedures

to reduce audit risk to an acceptable low level.

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This requires consideration of:

‰ inherent, control and detection risk; ‰ sampling and non-sampling risk.

2

SELECTING ALL ITEMS

Example 1

Suggest circumstances in which a 100% check of a class of transactions or account balances check may be necessary.

Solution

3

SELECTING SPECIFIC ITEMS

Example 2

Suggest reasons why it is unnecessary for an auditor to carry out a complete check of all the transactions and balances of a business.

Solution

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3.1

Judgmental selection

3.1.1

Factors to consider

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Knowledge of business

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Preliminary assessments of inherent and control risk

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Characteristics of the population being tested.

3.1.2

Specific items

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High-value or key items

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All items over a certain amount

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Items to obtain information

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Items to test procedures.

3.1.3

Main advantage

3.1.4

Main disadvantage

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Usually an efficient means of

4

AUDIT SAMPLING [ISA 530]

4.1

Definitions

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Audit sampling – applying procedures to less than 100% of items . . . such that all

sampling units have a chance of selection . . . in order to form a conclusion concerning the population.

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Error (in Audit sampling) – either a control deviations (in tests of control) or a

misstatement (in a substantive procedure).

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Anomalous error – an error that arises from an isolated event that has not recurred other

than on specifically identifiable occasions and is therefore not representative of errors in the population.

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Population − the entire set of data from which the auditor wishes to sample . . . . For

example, all items in an account balance or a class of transactions. A population may be divided into strata, or sub-populations, with each stratum being examined separately.

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Sampling risk − arises from the possibility that the auditor’s conclusion, based on a

sample, may be different from the conclusion that would be reached if the entire population were subjected to the same audit procedure. Two types:

(a) the risk the audit will conclude that control risk is lower than it actually is (for a test of control) or that a material error does not exist when in fact it does (for a

substantive test). This type of risk affects audit effectiveness and is more likely to lead to an inappropriate audit opinion

(b) the risk the auditor will conclude that control risk is higher than it actually is (for a test of control) or that a material error exists when in fact it does not (for a

substantive test). This type of risk affects audit efficiency as it would usually lead to additional work to establish that initials conclusions were incorrect.

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Confidence level – the mathematical complement of risk (e.g. 5% risk ≡ 95% confidence)

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Non-sampling risk − arises from factors that cause the auditor to reach an erroneous

conclusion for any reason not related to the size of the sample. For example, the auditor might use inappropriate procedures or misinterpret evidence and thus fail to recognize an error.

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Sampling unit − the individual items constituting a population, for example credit entries

on bank statements, sales invoices, trade receivable balances, or a monetary unit (e.g. $1).

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Statistical sampling – any approach to sampling that has the following characteristics

(a) random selection of a sample; and

A sampling approach that does not have characteristics (a) and (b) is considered non-statistical sampling.

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Stratification − the process of dividing a population into subpopulations, each of which is

a group of sampling units, which have similar characteristics (often monetary value).

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Tolerable error (or deviation rate) − the maximum error in the population that the auditor

is willing to accept (and still conclude that the result from the sample has achieved the audit objective).

‰ For substantive tests, this “precision” may be expressed as a monetary amount (which is less than overall materiality) or a percentage of population value.

‰ For tests of control, precision is the maximum rate of failure of an internal control that can be accepted in order to place reliance on it (and is therefore likely to be small).

4.2

Application

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Audit sampling can be applied using either non-statistical or statistical sampling methods (see later in this Session). Stages in the sampling process include:

‰ sample design ‰ sample selection

‰ performing audit procedures (“testing”) ‰ error evaluation.

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DESIGN

5.1

Basic principle

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Matters to be considered when designing an audit sample are:

‰ Test objectives; and

‰ Attributes of the population.

5.2

Sampling plan

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In practice a “Sampling plan” may be drawn up to cover ‰ audit objectives

‰ population and sampling unit (or attribute) ‰ definition of an error (or deviation)

‰ sample size

Matters to consider

Specific audit objectives

Note 1

Population and sampling unit and use of stratification

Sample size

Appropriate and complete

Note 2

Sampling unit

Note 3

Stratification (into sub-popn_s)

Note 4

Considerations

Sampling risk (acceptably

low?)

Tolerable error (= maximum error/deviation rate

willing to accept)

Expected error

Notes

(1) For example, “customers exist”.

(2) Must be appropriate (may be a “reciprocal” population) and complete. (3) An item number n (e.g. GRN)

(4) Involves dividing a population into subgroups (“strata”) to create relatively homogenous groups in which variations in characteristics are likely to be small.

5.3

Sample size

Example 3 — Tests of control

Solution

Effect on Sample Size

(1) Increase in intended reliance on accounting and internal control systems

(2) Increase in tolerable error

(3) Increase in the rate of deviation expected (“expected error”) (4) Increase in confidence level (i.e. decrease in risk)

(5) Increase in number of sampling units in the population.

Example 4 — Substantive procedures

For each of the following factors, decide whether the effect on sample size is an increase, decrease or no effect:

Solution

Effect on Sample Size

(1) Increase in inherent risk assessment (2) Increase in control risk assessment

(3) Increase in use of other substantive procedures aimed at the same assertion

(4) Increase in confidence level (i.e. decrease in risk) (5) Increase in tolerable error

(6) Increase in expected error (7) Stratification of the population

6

SELECTION

6.1

Basic principle

Items should be selected in such a way that all sampling units have a chance of selection.

6.2

Most commonly used methods

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Random number selection by use of random number tables or a computerised random

number generator.

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Systematic (also called “interval”) selection uses a constant interval between items

selected (with a random start). Value-weighted selection is a method which uses monetary unit values, rather than the items, as the sampling population.

CAUTION: The sampling units must not be structured in such a way that the sampling interval corresponds with a pattern in the population.

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Haphazard selection i.e. without following a structured technique, may be an acceptable

alternative (to random methods) provided that conscious bias and predictability are avoided.

6.3

Other methods

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Block sampling (e.g. all items on a particular page) is not generally appropriate because

populations may be structured so that items in a sequence have similar characteristics to each other but different characteristics to items elsewhere in the population.

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TESTING

7.1

Essential procedure

Audit procedures appropriate to the test objective should be performed on each item selected.

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If an inappropriate item is selected (e.g. a document which has been made “void”) an appropriately chosen replacement must be tested instead.

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If the planned procedure cannot otherwise be performed (e.g. if a customer does not reply to a direct confirmation request) a suitable alternative should be performed (e.g. examination of after-date cash receipts).

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SAMPLE RESULTS

8.1

Basic principles

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The auditor should:

‰ consider the sample results;

‰ consider the nature and cause of any identified errors; and ‰ their potential effect on:

−

the test objective

−

other audit areas

‰ evaluate sample results to confirm or revise the preliminary assessment of the relevant characteristic of the population.

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Consider qualitative aspects:

ISOLATED COMMON FEATURE

¾ Obtain corroborative evidence of anomalous error

Đ_{¾ Identify sub-population}

¾ Extend audit procedures in sub-stratum.

8.2

Error projection

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Monetary errors (i.e. in respect of substantive procedures) should be projected.

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The effect of projected error (on test objective and other audit areas) should be

considered.

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Compare:

‰ Projected error + Anomalous error vs

‰ Tolerable error.

Illustration 1

$ Population of trade receivables: 800,000

Sample value 274,330

Errors (e.g. overpricing of invoices) 4,311

Tolerable error 40,000 (5% of population)

Projected error (ratio method):

Error in sample ×

value Sample

value

Population _{= 4,311 ×}

330 , 274

000 ,

800 _{= $12,572}

Conclusion: Trade receivables are not materially overstated (as the potential

error is less than the tolerable error of $40,000).

8.3

Evaluation of results

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If projected error plus uncorrected anomalous error exceeds tolerable error, reassess sampling risk.

8.3.1

Tests of control

8.3.2

Substantive procedures

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If CR higher than originally assessed, modify planned procedures e.g.

‰ extend sample size

‰ test an alternative control ‰ extend substantive

procedures.

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If maximum potential and/or most likely error exceeds tolerable error

‰ request management adjust for identified errors

Illustration 2 — Evaluating the results of a test of controls

Summary of deviations:

DN ref.

No. of despatch notes not found 1 (13,685)

No. of despatch notes without invoices 4*

* include authorised cancellations (3) (17,345)

___

Actual deviations 2

___

Deviation rate:

1252 = 0.016

If the tolerable error is 1% (say) the sample size could be extended (to at least 200). If no further errors were found the deviation rate would be acceptable.

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STATISTICAL v NON-STATISTICAL SAMPLING

9.1

Statistical sampling

Involves

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use of random sample selection AND

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probability theory to

‰ evaluate sample results, and ‰ measure the sampling risk.

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Statistical sampling precludes the use of haphazard selection.

2 Main types

9.1.1

ATTRIBUTE SAMPLING

9.1.2

VARIABLES SAMPLING

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Sampling units either have a property (attribute) or they do not

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Concerns rates of occurrence of events not monetary amounts

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Therefore used in tests of controls.

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Sampling units can take a value within a continuous range

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Used to provide conclusions on monetary values

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Population value can be estimated by extrapolation.

Example 5

Suggest relative advantages/disadvantages of statistical sampling.

Solution

Advantages

Disadvantages

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9.2

Non-statistical sampling

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Any approach which does not fulfil ALL the conditions set out above in the definition of statistical sampling.

Example 6

Suggest the relative advantages/disadvantages of non-statistical sampling.

Solution

Advantages

Disadvantages

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FOCUS

You should now be able to:

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define audit sampling and explain the need for sampling;

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identify and discuss the differences between statistical and non-statistical sampling;

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discuss and provide relevant examples of the application of the basic principles of

statistical sampling and other selective testing procedures;

EXAMPLE SOLUTION

Solution 1 — Why 100%

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Population consists of a small number of large value items

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Items to which monetary materiality does not apply

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Unusual, one-off, or exceptional items

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Any area where the auditor is put upon enquiry

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Exceptionally high risk areas

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When the repetitive nature of a CIS operation makes 100% examination cost-effective

Solution 2 — Why not 100%

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Cost – expensive audit resources

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Time – financial statements unnecessarily delayed

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Users of a/cs do not require 100% accuracy

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Tedium – audit staff might miss errors

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Does not add value – few errors would normally be expected

Solution 3 — Tests of control: factors influencing sample size

Factor Sample

size Explanation

(1) ↑ Reliance on accounting and internal control systems (i.e. CR↓)

Increase ↑ To support a lower assessment of CR, will require larger sample sizes for tests of control

(2) ↑ Tolerable error Decrease

↓ If the auditor is prepared to accept, say, a 3% error rate rather than 1%, the amount of testing (and hence sample size) is reduced

(3) ↑ Expected error Increase ↑ If more errors are expected (perhaps because they are suggested by prior period or other findings) more work (i.e. greater sample size) is required. Note that if error rates are expected to be

high, CR would be 100% ∴ no tests of control.

(4) ↑ Confidence (i.e.

↓ Risk) Increase

Factor Sample

size Explanation

(5) ↑ Population Negligible The size of a large population has little, if any, effect on the sample size (e.g. a sample size may be 60 regardless of whether the population contains 1,600 items, 16,000 items or 160,000 items). For small populations, evidence is usually gathered by selective testing procedures other than audit sampling.

Solution 4 — Substantive procedures: factors influencing sample size

Factor

Sample

size

Explanation

(1) ↑ IR ↑ Increase Consider the audit risk model. If IR is higher, DR must be rendered lower – by doing more

substantive work (i.e. greater sample sizes). (2) ↑ CR ↑ Increase If CR is higher (i.e. less reliance on tests of

controls) more evidence must be obtained from substantive procedures (to render DR lower) (3) ↑ Other

substantive procedures

↓ Decrease If assurance is (to be) obtained by analytical procedures, less assurance is required from tests of detail.

(4) ↑ Confidence ↑ Increase As for Solution 3 (5) ↑ Tolerable

error ↓ Decrease As for Solution 3 (6) ↑ Expected error ↑ Increase As for Solution 3

(7) Stratification ↓ Decrease The aggregate of the sample sizes from the strata will usually be less than that of a single sample drawn from the whole population

Solution 5 — Statistical sampling

Advantages

Disadvantages

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Imposes more formal discipline to

planning audit of a population

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Expense of implementation

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Objectively determines sample sizes

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Sample sizes may be “too large”

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Evaluates test results more precisely

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May be time-consuming (e.g. manually determining cumulative monetary amounts)

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Quantifies sampling risk

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Staff training

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Use of judgement is not precluded (as it

is required to set objectives and evaluate results).

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In tests of control, qualitative aspects of error evaluation cannot be statistically analysed

Solution 6 — Non-statistical sampling

Advantages

Disadvantages

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Approach used for many years, is well

understood and refined by experience

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Sample sizes may be too small to satisfy stated objectives

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Can use greater judgement and expertise

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Sampling risk cannot be quantified

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Non-random selection may be

quicker/more cost effective

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Statistical sampling may be cheaper if CAATs used

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No special knowledge of statistics

required

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Sample sizes may be unnecessarily large

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Less expensive to apply (usually)

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Personal bias in sample selection may be unavoidable (e.g. if using