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Illustration 4 Comparative financial statements
Report illustrative of the circumstances described as follows:
Auditor is required to report on both the current period financial statements and the prior
period financial statements in connection with the current year’s audit.
The auditor’s report on the prior period, as previously issued, included a qualified opinion.
The matter giving rise to the modification is unresolved.
The effects or possible effects of the matter on the current period’s figures are material to
both the current period financial statements and prior period financial statements and
require a modification to the auditor’s opinion.
INDEPENDENT AUDITORS' REPORT
Basis for Qualified Opinion
As discussed in Note X to the financial statements, no depreciation has been provided in the
financial statements, which constitutes a departure from International Financial Reporting
Standards. Based on the straight-line method of depreciation and annual rates of 5% for the
building and 20% for the equipment, the loss for the year should be increased by xxx in 20X1
and xxx in 20X0, property, plant and equipment should be reduced by accumulated
depreciation of xxx in 20X1 and xxx in 20X0, and the accumulated loss should be increased by
xxx in 20X1 and xxx in 20X0.
Qualified Opinion
In our opinion, except for the effects of the matter described in the Basis for Qualified Opinion
paragraph, the financial statements present fairly, in all material respects, (or give a true and
fair view of) the financial position of ABC Company as at December 31, 20X1 and 20X0 and
(of) its financial performance and its cash flows for the years then ended in accordance with
International Financial Reporting Standards.
..
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16. ANALYTICAL PROCEDURES (INCORPORATING ISA 520)
16.1. Objective
16.1.1 The objectives of the auditor are:
To use analytical procedures as risk assessment procedures
To obtain relevant and reliable audit evidence when using substantive analytical
procedures; and
To design and perform analytical procedures near the end of the audit that assist the
auditor when forming an overall conclusion as to whether the financial statements are
consistent with the auditor’s understanding of the entity.
16.2. Definition
16.2.1 ISA 520 defines the term “analytical procedures” as: “evaluations of financial
information through analysis of plausible relationships among both financial and non-financial
data. Analytical procedures also encompass such investigation as is necessary of identified
fluctuations or relationships that are inconsistent with other relevant information or that differ
from expected values by a significant amount.”
16.3. Purposes
16.3.1 Analytical procedures are used for the following purposes:
At the planning stage, as a risk assessment procedure to obtain an understanding of the
entity and its environment;
As substantive procedures during the execution phases, when their use can be effective
and/or efficient in obtaining relevant and reliable audit evidence; and
At the completion stage, i.e. near the end of the audit, to assist the auditor when forming
an overall conclusion as to whether the financial statements are consistent with the
auditor’s understanding of the entity and with other audit evidence obtained during the
course of the audit.
16.3.2 ISA 520 deals with:
analytical procedures as substantive procedures, and
analytical procedures at the completion stage.
16.3.3 The use of analytical procedures at the planning stage and at the completion phase is
required by ISA 520. The use of analytical procedures as substantive procedures is optional.
16.4. Planning stage
16.4.1 Analytical procedures at the planning stage are considered under ISA 315 and are dealt
with in chapter 7 of this manual.
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16.5. Analytical procedures as substantive procedures
16.5.1 Substantive analytical procedures involve a comparison of amounts or relationships in
the financial report with an expectation developed from information obtained from
understanding the entity and other audit evidence.
16.5.2 Substantive analytical procedures alone are not a sufficient response to
significant risks
The use of substantive analytical procedures by themselves is not considered an appropriate
response to address a significant risk. When the approach to significant risks consists only of
substantive procedures, the audit procedures can consist of:
Tests of details only, or
A combination of tests of details and substantive analytical procedures.
16.5.3 The decision about which audit procedures to perform, including whether to use
substantive analytical procedures, is based on the auditor’s judgment about the expected
effectiveness and efficiency of the available audit procedures to reduce audit risk at the
assertion level to an acceptably low level.
16.5.4 To use an analytical procedure as a substantive procedure, the auditor should design
the procedure to reduce the risk of not detecting a material misstatement in the relevant
assertion to an acceptably low level. This means that the expectation of what the recorded
amount should be is precise enough to indicate the possibility of a material misstatement,
either individually or in the aggregate, in other misstatements.
16.5.5 For audit planning purposes, substantive analytical procedures may be grouped into
three distinct levels based on the level of assurance obtained. These are described below.
Impact on reducing audit risk Description
Highly effective (high level of risk reduction) Procedure is intended to be the primary source
Moderately effective of evidence regarding a financial report
Limited assertion. It effectively proves the recorded
amount.
Procedure is only intended to corroborate
evidence obtained from other procedures. A
moderate level of assurance is obtained.
Basic procedures, such as comparing an
amount in the current period to a previous
period, are useful but only provide a limited level
of assurance.
16.5.6 When designing and performing substantive analytical procedures, either alone or in
combination with tests of details, as substantive procedures, the auditor shall:
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Determine the suitability of particular substantive analytical procedures for given
assertions, taking account of the assessed risks of material misstatement and tests of
details, if any, for these assertions;
Evaluating the reliability of data from which the auditor’s expectation of recorded
amounts or ratios is developed, taking account of source, comparability, and nature and
relevance of information available, and controls over preparation;
Develop an expectation of recorded amounts or ratios and evaluate whether the
expectation is sufficiently precise to identify a misstatement that individually or when
aggregated with other misstatements, may cause the financial statements to be
materially misstated; and
Determine the amount of any difference of recorded amounts from expected values that
is acceptable without further investigation of the difference.
16.5.7 When relying on analytical procedures as a substantive procedure, the auditor will
ordinarily inquire of management as to the availability and reliability of information needed to
apply substantive analytical procedures, and the results of any such analytical procedures
performed by the entity. It may be efficient to use analytical data prepared by management,
provided the auditor is satisfied that such data is properly prepared.
16.6. Suitability of particular substantive analytical procedures for given assertions
16.6.1 Substantive analytical procedures are generally more applicable to large volumes of
transactions that tend to be predictable over time. The application of planned analytical
procedures is based on the expectation that relationships among data exist and continue in the
absence of known conditions to the contrary. However, the suitability of a particular analytical
procedure will depend on the auditor’s assessment of how effective it will be in detecting a
misstatement that, individually or when aggregated with other misstatements may cause the
financial statements to be materially misstated.
16.6.2 In determining the suitability of substantive analytical procedures given the assertions,
consideration should be given to the following:
The assessment of the risk of material misstatement - The auditor considers the
understanding of the entity and its internal control, the materiality and likelihood of
misstatement of the items involved, and the nature of the assertion in determining
whether substantive analytical procedures are suitable. For example, if controls over
sales order processing are weak or deficient, the team may place more reliance on tests
of details rather than on substantive analytical procedures for assertions related to
receivables. As another example, when inventory balances are material, one does not
ordinarily rely only on substantive analytical procedures when performing audit
procedures on the existence assertion.
Reliability of the data
Precision of Expectations – Whether the expectation is sufficiently precise to identify a
material misstatement at the desired level of assurance.
Allowable differences - The acceptable level of any differences in recorded amounts
from expected values.
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Any tests of details directed toward the same assertion - Substantive analytical
procedures may also be considered appropriate when tests of details are performed on
the same assertion. For example, when auditing the collectability of accounts receivable,
one may apply substantive analytical procedures to an aging of customers’ accounts in
addition to tests of details on subsequent cash receipts.
16.7 The reliability of the data
16.7.1 The reliability of data is influenced by:
its source and
its nature
and is dependent on the circumstances under which it is obtained. Accordingly, the following
are relevant when determining whether data is reliable for purposes of designing substantive
analytical procedures:
Sources of information available - For example, information is ordinarily more reliable
when it is obtained from independent sources outside the entity, or from within the entity
developed by persons not directly responsible for its accuracy.
Comparability of the information available - For example, broad industry data may need
to be supplemented to be comparable to that of an entity that produces and sells
specialised products, and disaggregated information may provide more reliable and
precise expectations than aggregated information.
Nature and relevance of the information available - For example, whether budgets have
been established as results to be expected rather than as goals to be achieved.
Controls over the preparation of the information that are designed to ensure its
completeness, accuracy and validity. For example, controls over the preparation, review
and maintenance of budgets.
Prior year knowledge and understanding
16.7.2 When testing the reliability of data, the engagement team should consider testing the
controls, if any, over the entity’s preparation of information which is to be used by the
engagement team in applying substantive analytical procedures. When such controls are
effective, the team develops greater confidence in the reliability of the information and,
therefore, in the results of substantive analytical procedures.
16.8. Whether the expectation is sufficiently precise
16.8.1 In assessing whether the expectation can be developed sufficiently precisely to identify
a misstatement that, when aggregated with other misstatements, may cause the financial
statements to be materially misstated, include:
The accuracy with which the expected results of substantive analytical procedures can be
predicted. For example, the auditor may expect greater consistency in comparing gross
profit margins from one period to another than in comparing discretionary expenses, such
as research or advertising.
The degree to which information can be disaggregated. For example, substantive
analytical procedures may be more effective when applied to financial information on
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individual sections of an operation or to financial statements of components of a
diversified entity, than when applied to the financial statements of the entity as a whole.
The availability of the information, both financial and non-financial. For example, the
auditor may consider whether financial information, such as budgets or forecasts, and
non-financial information, such as the number of units produced or sold, is available to
design substantive analytical procedures. If the information is available, the auditor may
also consider the reliability of this information.
16.9. Amount of difference of recorded amounts from expected values that is acceptable
16.9.1 In designing and performing substantive analytical procedures, the auditor considers
the amount of difference from expectation that can be accepted without further investigation.
16.9.2 This consideration is influenced primarily by materiality and consistency with the desired
level of assurance, taking account of the possibility that a misstatement, individually or when
aggregated with other misstatements, may cause the financial statements to be materially
misstated. ISA 330 requires the auditor to obtain more persuasive audit evidence the higher
the auditor’s assessment of risk. Accordingly, as the assessed risk increases, the amount of
difference considered acceptable without further investigation decreases in order to achieve
the desired level of persuasive evidence.
16.9.3 When the engagement team performs substantive procedures at an interim date and
plans to perform substantive analytical procedures with respect to the intervening period, the
team considers how the factors surrounding the procedure affect the ability to obtain sufficient
appropriate audit evidence for the remaining period. This includes considering whether the
period-end balances of the particular classes of transactions or account balances are
reasonably predictable with respect to amount, relative significance, and composition.
16.10. Approach to using analytical review as substantive procedures
16.10.1 If it is decided to use analytical procedures as substantive procedures, a methodical
approach is essential. The process can be summarised as a series of stages:
Define the item or relationship to be used
16.10.2 Consider first of all:
• The relevance and reliability of the relationship being considered for testing.
• The complexity of the relationship.
• The level of detail available and required.
• Any shortcomings, such as the possibility of over-simplification or circular proofs of
relationships.
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Define the objectives of the review process
16.10.3 For example, it may be desired to use analytical review techniques to decide on the
completeness or accuracy of sales.
Consider any factors that could lead to deviations from expected results
16.10.4 For sales, these could include year-end cut-off procedures or changes in the price or
product mix.
Determine the examination methods
16.10.5 This could include the type of procedure to be used, whether complex techniques are
to be used, and also whether the use of computerised comparisons is likely.
Define a significant deviation from expected results
16.10.6 Factors to consider would include:
• The materiality level chosen.
• The level of confidence chosen.
• The direction of the test (testing for over or understatement).
• The expected sizes of deviation.
Specify the reliance desired (i.e. the assurance required from analytical review
procedures)
16.10.7 Factors to consider would include:
• The nature of the assertions being audited.
• The extent to which assurance will be gained from analytical procedures and other
tests.
• The risks of error in the population.
• The precision of the procedure to be adopted.
16.11. Examine any Deviations from Expected Results
Identify deviations
16.11.1 Deviations may be of a number of different types:
Normal deviations (business trends, seasonal changes, trading cycles).
Isolated deviations (those caused by changes in accounting policies or unusual items).
Abnormal deviations (those caused by accounting policies applied incorrectly, inadequate
accounting or irregularities).
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Investigate significant deviations
16.11.2 If comparisons between the amounts predicted and the amounts recorded reveal
unexpected deviations (such as unexpected variations between prior year and current year
recorded figures, based on our knowledge of activities which have occurred during the year),
these should be discussed with the management of the entity.
16.11.3 Management explanations should not be accepted in isolation. It may be that they are
supported by audit evidence already available.
16.11.4 Further procedures may, however, be necessary. These may include:
Extending the analytical procedure: The original procedure may need to be adjusted
following additional factors identified in testing. The reliability of such factors will need to
be corroborated in the same way as the factors used originally. In some cases, a detailed
analysis of individual accounts may be necessary to determine whether the explanations
received are acceptable.
Examining documentation: This may be necessary where the difference is caused by a
small number of unusual or infrequently occurring transactions.
Making enquiries of others, if possible: Explanations from management in one sector may
be confirmed by management in a different one.
Conducting additional substantive tests, if the explanations received appear inadequate.
16.12. Evaluating the results and forming conclusions
16.12.1 The degree of reliance to be placed on analytical tests needs to be considered.
Reliance may range from total reliance (such as proving an account balance in total) to no
reliance.
16.12.2 Factors to consider when assessing how much reliance can be placed on the results
could include:
Any analytical procedures that show similar results.
The extent variations have been corroborated by explanations or other audit procedures
directed towards the same objectives.
The materiality of the items involved (for example, when inventory balances are material,
the auditor does not rely only on analytical procedures in forming his conclusions).
16.12.3 In order to be able to place high reliance on analytical review (and therefore assess
analytical risk as low), the engagement team must be satisfied that the results represent
relevant and reliable evidence, in some cases corroborated by explanations and other
evidence.
16.12.4 Care must also be taken to ensure that data used to determine an expectation is not
data used by the client in preparing the information being tested, eg when testing the recorded
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payroll.expense, an auditor should not use the client’s payroll, which forms the source of the
payroll expense, to obtain evidence about the payroll expense..
16.13. Recording of analytical procedures
16.13.1 Analytical procedures must be documented in order to provide valid support for the
audit opinion.
16.13.2 Any working papers which detail analytical procedures used as substantive tests
should include:
The objectives of the tests.
The sources of the information used.
Details of the procedures performed (such as trends, ratios, or reasonableness tests).
The basis of calculations and the documentation of the expectations against which to
compare the outcomes.
All assumptions used.
Other factors affecting the procedures used.
Any predictions made and tolerable ranges or results accepted.
The extent and nature of variations, highlighting significant fluctuations.
Explanations obtained for variations, including their origin.
Tests carried out to verify explanations received.
Any re-calculations and other procedures considered necessary.
The extent of any effect on the audit plan.
The conclusions reached, including an estimate of the amount of misstatements.
16.13.3 In conclusion, while analytical procedures may be used as substantive procedures,
consideration should be given to the cost-benefit analysis of using them compared to other
substantive or compliance procedures that may be more cost effective. Even when analytical
procedures are used, the procedures adopted, the reliability of the data used in the
procedures, the expectations developed and the conclusions should be recorded as part of the
audit work.
16.14. Analytical procedures at the completion stage
16.14.1 The auditor shall design and perform analytical procedures near the end of the audit
that assist the auditor when forming an overall conclusion as to whether the financial
statements are consistent with the auditor’s understanding of the entity.
16.14.2 The conclusions drawn from the results of these analytical procedures are intended to
corroborate conclusions formed during the audit of individual components or elements of the
financial statements. This assists the auditor to draw reasonable conclusions on which to base
the auditor’s opinion.
16.14.3 The result of analytical procedures near the end of the audit may identify a previously
unrecognised risk of material misstatement. In such circumstances ISA 315 requires the
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auditor to revise the auditor’s assessment of the risk of material misstatement and modify the
further planned audit procedures accordingly.
16.14.4 The audit procedures performed near the end of the audit may be similar to those that
would be used as risk assessment procedures.
16.15. Types of analytical procedures
16.15.1 There are a number of techniques that can be used to perform analytical procedures.
The objective is to select the most appropriate technique to provide the intended levels of
assurance and precision. These techniques include:
Ratio analysis
16.15.2 Ratio analysis procedures compare relationships between items in the accounts over
time, or between different entities. Ratio analysis may involve comparison of financial ratios, or
items compared with other items (for example, cost of sales as a percentage of sales).
16.15.3 In order for ratio analysis to be helpful, the ratios compared must have been calculated
on a consistent basis and the relationship between the factors in the ratio should be stable.
Trend analysis
16.15.4 This is the analysis of changes in a given item over time.
Trend analysis procedures include:
• Period by period comparisons.
• Graphs of recent and historic results.
• Weighted averages of recent and historical results.
• More complicated statistical techniques such as regression analysis.
16.15.5 In conducting trend analysis care must be taken to ensure that explanations provided
by a client for trends or movements are corroborated by further investigation to the extent
considered necessary to achieve the desired level of assurance.
Pattern analysis
16.15.6 Detailed comparisons of data, for example with that of other periods or with operating
budgets.
16.15.7 Each technique has its own particular strengths and weaknesses that need to be
considered when designing procedures. A complex technique such as regression analysis may
provide statistically reliable conclusions about a recorded amount. However a simple technique
may suffice. For example, multiplying the number of apartments by the approved rental rates
per leases and adjusting the result for actual vacancies may provide a reliable and precise
estimate of the rental revenue for a property rental business.
16.15. 8 The use of ratios, and calculations using techniques above, are not of themselves
likely to provide audit evidence. The auditor must analyse the results, ratios etc, and compare
them with expected results. This requires the auditor to critically analyse the explanations
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provided by the client for the results of analysis, to the extent necessary to provide the auditor
with the level of audit evidence required from the test.
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APPENDIX I: KEY BUSINESS RATIOS
This Appendix provides illustration on how to calculate some ratios that may be used when
carrying out analytical procedures. There may be other suitable basis for calculating ratios.
These may be equally acceptable, provided that the ratios are used consistently and variations
in the formulae documented.
Ratios are not of themselves likely to provide valuable audit evidence. The auditor must
analyse the ratios, and compare them with expected results. This requires the auditor to
critically analyse the explanations provided by the client for the results of analysis, to the extent
necessary to provide the auditor with the level of audit assurance required from the test.
1. FINANCIAL STATUS
These ratios indicate the likelihood of the entity meeting its short or long-term obligations.
Acid test
Current Assets (excluding Inventories)
Current Liabilities
This ratio demonstrates the entity’s immediate liquidity position. It should normally exceed 1.0.
Current ratio
Current Assets
Current Liabilities
A ratio of over 2.0 indicates the probability that payments to creditors can be met as they fall
due. A low ratio indicates a potential working capital shortage or a going concern problem.
Gearing
(Total Liabilities / Total Liabilities plus Share Capital and Reserves) x 100%
This ratio shows the extent to which the entity is financed by creditors and debt. A high ratio
may indicate a high debt burden which in turn could signify liquidity and going concern
problems.
Property, plant and equipment to long-term liabilities
(Property, plant & equipment / long-term liabilities) x 100%
This ratio indicates the extent to which long-term finance is used to finance acquisition of
property, plant and equipment. A high score co-existing with significant current liabilities may
indicate property, plant and equipment are financed out of current liabilities, which could
indicate going concern difficulties.
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Property, plant and equipment to equity
(Property, plant & equipment / share capital plus reserves) x 100%
An entity may be under-capitalised if the ratio is significantly high.
Current liabilities to equity
(Current liabilities / Share capital plus reserves) x 100%
This shows the contrast between the funds short-term creditors have placed with the entity,
with the funds invested by the owners. The higher the ratio, the less the creditors' security.
Interest cover
Profit before interest & tax
Interest payable
The ratio shows the extent to which profits are available to service borrowing costs. The higher
the ratio, the more likely that the entity will be able to meet its debt service costs. A low ratio
could signify reliance on other sources to fund debt servicing costs and likely liquidity
problems.
2. ASSET UTILITY
These ratios show how the entity uses and controls its assets. An annualised denominator
may be used. However, in situations where the denominator is not evenly spread throughout
the year, it may be more appropriate to use annual figures instead of annualised figures.
Inventory turnover days
Annual method -(inventories / purchases) x 365
This ratio is a guide to the speed at which inventories are moving, and varies widely between
different industries. A low figure may indicate situations such as excessive levels of inventory,
high storage and financing costs, obsolete or slow moving inventory. A high figure may be
evidence of insufficient supplies and hence lost sales opportunities. A high figure may also
signify lack of credit facilities with suppliers or the inability of the suppliers (especially key
suppliers) to supply goods. This could give rise to doubts over the entity’s going concern
status.
FIFO Method -Total purchases for a period working back from balance date until total
equals inventories on hand at balance date.- period of those Purchases is time for
which inventory has been on hand.
A high number of days gives the same indications as a low turnover using the Annual method.
This measure provides more reliable information than the Annual method, as it compares
inventories on hand with purchases during the period when the items were put into the
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inventory figure being used. The Annual method does not take into account seasonality of
activity, and compares inventories on hand with purchases made up to 12 months ago, which
may have been used for inventories which have since been sold, and are not part of balance
date inventories.
Trade receivable turnover days
• Annual method: (Trade receivables / turnover) x 365
• FIFO Method: Total sales for a period working back from balance date until total
equals trade receivables at balance date.- period of those sales is the time for which
receivables have been not collected..
A high ratio for the annual method and a high number of days for the FIFO Method may
indicate potential cash flow and working capital problems, or impairment of receivables.
This FIFO measure provides more reliable information than the annual method, as it compares
trade receivables at balance date with sales during the period when the items were put into the
trade receivables figure being used. The annual method does not take into account seasonality
of activity, and the level of collections for the period prior to balance date has less effect on the
measure as it is also affected by collections earlier in the year, and compares receivables at
balance date with sales made up to 12 months ago, which may have been since collected, and
are not part of balance date trade receivables..
Working capital turnover days
(Working capital / turnover) x 365
A high ratio may demonstrate that the entity is over-trading. A low ratio may indicate excessive
funds being tied up in working capital.
As for measures above, a FIFO method might provide more meaningful information.
Trade payables turnover days
(Trade payables / purchases) x 365
This shows how the entity is paying its suppliers. A high ratio may indicate that the firm is using
supplier credit to finance its operations.
As for measures above, a FIFO method might provide more meaningful information.
3. PROFITABILITY AND RETURN
Gross profit margin
(Gross profit / turnover) x 100%
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Net profit margin
(Profit before tax / turnover) x 100%
Return on shareholder’s funds
(Profit before tax / average shareholder’s Funds) x 100%
Return on capital employed
(Profit before tax / total Assets less Current Liabilities) x 100%
Return on assets
(Profit before tax / average total assets) x 100%
The ratios differ between various businesses and sectors and any conclusions should be
based on trends and expectations developed for each entity.
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APPENDIX II: EXAMPLES OF AREAS WHERE ANALYTICAL PROCEDURES MAY BE
USED AS SUBSTANTIVE PROCEDURES
Payroll
• Determine the payroll per department (especially for similar type of workers) by
multiplying the salary per person by the number of people employed in that department
and comparing this with the departmental payroll.
Production
• Determining the annual production by multiplying the plant capacity with the number of
days of operations and comparing this with actual production.
Turnover
• Determining the annual turnover by multiplying the number of units sold by the average
selling price and adjusting this with returns (may only be possible if the client sells a few
products) and comparing this with annual turnover.
• Comparing the actual turnover with theoretical turnover (determined by multiplying the
capacity with the price) and reconciling any material differences. This would be possible
in educational institutions, airlines, hospitals, hotels and similar businesses which have
a fixed capacity.
• Prediction of total rental income on a building divided into apartments, taking the rental
rates, the number of apartments and vacancy rates into consideration.
• Comparing the theoretical output VAT based on the turnover and the VAT rates, and
reconciling any differences to turnover and the actual output VAT.
Gross profit
• Determining the theoretical gross profit margin or the weighted average gross profit
margin by reference to the selling price and the cost of goods sold and comparing this
to the actual gross profit margin (may only be possible if the client sells a few products
or has a fixed mark-up policy for a range of products).
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17. SAMPLING (INCORPORATING ISA 530)
17.1. Objective and definitions
17.1.1 The objective of the auditor, when using audit sampling, is to provide a reasonable
basis for the auditor to draw conclusions about the population from which the sample is
selected.
17.1.2 As per ISA 530, “Audit Sampling (sampling) involves the application of audit procedures
to less than 100% of items within a population of audit relevance such that all sampling units
have a chance of selection in order to provide the auditor with a reasonable basis on which to
draw conclusions about the entire population.
Audit sampling can use either a statistical or a non-statistical approach”.
17.1.3 Other key definitions as per ISA 530 are:
Error - this is defined as either control deviations, when performing tests of controls, or
misstatements, when performing tests of details. Similarly, total error is used to mean either
the rate of deviation or total misstatement.
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.
Population - The entire set of data from which a sample is selected and about which the
auditor wishes to draw conclusions. For example, all of the items in a class of transactions
or account balance constitute a population. A population may be divided into strata, or sub-
populations, with each stratum being examined separately. The term population is used to
include the term stratum.
Sampling risk - Arises from the possibility that the auditor’s conclusion, based on a sample
may be different from the conclusion reached if the entire population were subjected to the
same audit procedure. There are two types of sampling risk:
• The risk the auditor will conclude, in the case of a test of controls, that controls are more
effective than they actually are, or in the case of a test of details, that a material error
does not exist when in fact it does. This type of risk affects audit effectiveness and is
more likely to lead to an inappropriate audit opinion; and
• The risk the auditor will conclude, in the case of a test of controls, that controls are less
effective than they actually are, or in the case of a test of details, that a material error
exists when in fact it does not. This type of risk affects audit efficiency, as it would
usually lead to additional work to establish that initial conclusions were incorrect.
The mathematical complements of these risks are termed confidence levels.
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, ordinarily, the
auditor finds it necessary to rely on audit evidence that is persuasive rather than
conclusive, the auditor might use inappropriate audit procedures, or the auditor might
misinterpret audit evidence and fail to recognise an error.
Sampling unit - Means the individual items constituting a population, for example, cheques
listed on deposit slips, credit entries on bank statements, sales invoices or debtors’
balances, or a monetary unit.
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Statistical sampling - Any approach to sampling that has the following characteristics:
• Random selection of a sample; and
• Use of probability theory to evaluate sample results, including measurement of sampling
risk. A sampling approach that does not have characteristics (a) and (b) is considered
non-statistical sampling.
Stratification - The process of dividing a population into sub-populations, each of which is a
group of sampling units, which have similar characteristics (often monetary value).
Tolerable misstatement - A monetary amount set by the auditor in respect of which the
auditor seeks to obtain an appropriate level of assurance that the monetary amount set by
the auditor is not exceeded by the actual misstatement in the population. ) When designing
a sample, the auditor determines tolerable misstatement in order to address the risk that
the aggregate of individually immaterial misstatements may cause the financial statements
to be materially misstated and provide a margin for possible undetected misstatements.
Tolerable misstatement is the application of performance materiality, as defined in ISA 320,
to a particular sampling procedure. Tolerable misstatement may be the same amount or an
amount lower than performance materiality.)
Tolerable rate of deviation – a rate of deviation from prescribed internal control procedures
set by the auditor in respect of which the auditor seeks to obtain an appropriate level of
assurance that the rate of deviation set by the auditor is not exceeded by the actual rate of
deviation in the population.
17.2. Factors determining the use of sampling
17.2.1 ISA 530 requires that when using either statistical or non-statistical sampling
procedures, the auditor should design and select an audit sample, perform audit procedures
thereon and evaluate the sample results, so as to obtain sufficient and appropriate audit
evidence.
17.2.2 The following factors need to be considered when determining the use of sampling:
Audit objectives
17.2.3 The objectives of the sampling process should be considered, in particular, whether
overstatement or understatement is being tested, or whether controls are being tested.
17.2.4 If the test is for overstatement, the higher the value of the item, the bigger the potential
for restatement. The selection of items for audit examination should be biased towards higher
value items. If the test is for understatement, selection should concentrate on items where
there is a risk of material understatement (note: errors of understatement can appear in items
of any value, including nil balances).
17.2.5 A control test is designed to test procedures. Accordingly, the value of the item being
tested is not a factor in determining whether or not a procedure has been applied. All items in a
population, irrespective of their size, should be considered equally in selecting a sample for a
control test. Audit sampling for control tests is generally appropriate when application of the
control leaves evidence of performance.
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Assurance required
17.2.6 Each audit test should be designed to be no more extensive than is necessary to obtain
sufficient evidence or to keep the audit risk within acceptable limits. This means that materiality
will be a key factor.
17.3. Sample design, size and selection of items for testing
17.3.1 When designing an audit sample, the auditor shall consider the objectives of the audit
procedure and the characteristics of the population from which the sample will be drawn.
Objectives
17.3.2 The engagement team first considers the specific objectives to be achieved and the
combination of audit procedures which is likely to best achieve those objectives. Consideration
of the nature of the audit evidence sought and possible error conditions or other characteristics
relating to that audit evidence will assist the team in defining what constitutes an error and
what population to use for sampling.
The engagement team also considers what conditions constitute an error by reference to the
objectives of the audit procedure. A clear understanding of what constitutes an error is
important to ensure that all and only those conditions that are relevant to the objectives of the
audit procedure are included in the projection of errors. For example, in a test of details
relating to the existence of accounts receivable, such as confirmation, payments made by the
customer before the confirmation date but received shortly after that date by the client are not
considered an error. Also, mispostings between customer accounts do not affect the total
accounts receivable balance. Therefore, it is inappropriate to consider this as an error in
evaluating the sample results of this particular audit procedure, even though it may have an
important effect on other areas of the audit, such as the assessment of the likelihood of fraud
or the adequacy of the allowance for doubtful accounts.
17.3.3 When performing tests of controls, one generally makes an assessment of the rate of
error one expects to find in the population to be tested. This assessment is based on the
engagement team’s understanding of the design of the relevant controls and whether they
have been implemented or the examination of a small number of items from the population.
Similarly, for tests of details, the engagement team generally makes an assessment of the
expected amount of error in the population. These assessments are useful for designing an
audit sample and in determining sample size. For example, if the expected rate of error is
unacceptably high, tests of controls will normally not be performed. However, when performing
tests of details, if the expected amount of error is high, a 100% examination or the use of a
large sample size may be appropriate.
Population
17.3.4 It is important for the engagement team to ensure that the population is:
Appropriate to the objective of the audit procedure, which will include consideration of the
direction of testing. For example, if the engagement team’s objective is to test for
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overstatement of accounts payable, the population could be defined as the accounts
payable listing. On the other hand, when testing for understatement of accounts payable,
the population is not the accounts payable listing but rather subsequent disbursements,
unpaid invoices, suppliers’ statements, unmatched receiving reports or other populations
that provide audit evidence of understatement of accounts payable; and
Complete. For example, if one intends to select payment vouchers from a file, conclusions
cannot be drawn about all vouchers for the period unless one is satisfied that all vouchers
have in fact been filed. Similarly, if the engagement team intends to use the sample to draw
conclusions about whether a control activity operated effectively during the financial
reporting period, the population needs to include all relevant items from throughout the
entire period. Obtaining evidence that the population is complete is a requirement of ISA
500 which deals with Audit Evidence.
17.3.5 Audit efficiency may further be improved if the population is stratified by dividing it into
discrete sub-populations which have an identifying characteristic. Stratification reduces the
variability of items within each stratum, thereby allowing sample size to be reduced without a
proportional increase in sampling risk. Sub-populations need to be carefully defined such that
any sampling unit can only belong to one stratum.
17.3.6 When performing tests of details, a class of transaction or account balance is often
stratified by monetary value. This allows greater audit effort to be directed to the larger value
items which may contain the greatest potential monetary error in terms of overstatement.
Similarly, a population may be stratified according to a particular characteristic that indicates a
higher risk of error, for example, when testing the valuation of accounts receivable, balances
may be stratified by age.
17.3.7 The results of audit procedures applied to a sample of items within a stratum can only
be projected to the items that make up that stratum. To draw a conclusion on the entire
population, the engagement team will need to consider the risk of material misstatement in
relation to whatever other strata make up the entire population. For example, 20% of the items
in a population may make up 90% of the value of an account balance. The engagement team
may decide to examine a sample of these items, and evaluate the results of this sample to
reach a conclusion on the 90% of value separately from the remaining 10% (on which a further
sample or other means of gathering audit evidence will be used, or which may be considered
immaterial).
17.3.8 If a class of transactions or account balance has been divided into strata, the
misstatement is projected for each stratum separately. Projected misstatements for each
stratum are then combined when considering the possible effect of misstatements on the total
class of transactions or account balance
17.4. Statistical versus non-statistical sampling
17.4.1 The decision whether to use a statistical or non-statistical sampling approach is a
matter for the auditor’s judgement regarding the most efficient manner to obtain sufficient
appropriate audit evidence in the particular circumstances. The method of sample selection will
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affect not only the sample sizes used but also the method by which errors will be evaluated.
Sample size is not a valid criterion to distinguish between statistical and non-statistical
approaches.
17.4.2 With statistical sampling, sample items are selected in a way that each sampling unit
has a known probability of being selected. With non-statistical sampling judgement is used to
select sample items.
17.4.3 Because the purpose of sampling is to provide a reasonable basis for the auditor to
draw conclusions about the population from which the sample is selected, it is important that
the auditor selects a representative sample, so that bias is avoided, by choosing sample items
which have a characteristic typical of the population.
17.4.4 The advantages of statistical sampling are:
It imposes a more formal discipline towards planning the audit of a population.
The required sample size is determined objectively using statistical tables.
Each sampling unit has a known probability of being selected.
The evaluation of test results is made more precisely and the sampling risk is quantified.
17.4.5 The disadvantages of statistical sampling are:
Lack of judgement (however, note that judgement is used to set the objectives for the
sample and to evaluate the results of tests).
Statistical procedures can take time to set up and to implement. However, in practice, little
extra time is incurred in planning a statistical sampling approach and statistical procedures
are particularly cost effective if:
• there is a large population; and
• a large number of small items are being examined, rather than a small number of larger
items.
Statistical sampling procedures impact on sample sizes. It is possible that statistical sample
sizes could be higher than those derived at using judgmental methods (this however,
depends on the judgement of the person carrying out the test).
17.4.6 For different populations to be audited, different sampling procedures should be
considered; key factors in deciding whether or not to use statistical sampling are the extent of
reliance on substantive tests of detail, and the desire to quantify risk and obtain assurance.
17.5. Documenting the sampling process
17.5.1 The following should be stated whenever sampling procedures are used:
The objectives of the procedure and the definitions of error.
The definition of the population (and the population value if relevant).
How it was ensured that selection was made from a complete population.
The definition of the sampling unit.
The tolerable error level or rate.
The size of the sample and the sampling interval.
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The method of sample selection.
The nature, causes and follow-up of errors found.
Sample evaluation procedures.
The overall audit conclusions.
NOTE: All errors found above the tolerable error level should be included in the overall
summary of unadjusted errors schedule.
17.6. Methods of selecting the sample
17.6.1 As per ISA 530, “The auditor should select items for the sample in such a way that each
sampling unit in the population has a chance of selection”.
17.6.2 The sample selection methods will depend on whether statistical or judgemental
sampling is used. The principal methods of sample selection are as follows:
100% testing (although this is not sampling) - It may be most appropriate to examine the
entire population of items that make up a class of transactions or account balance (or a
stratum within that population). 100% examination is unlikely in the case of tests of
controls; however, it is more common for tests of details. For example, 100% examination
may be appropriate when the population constitutes a small number of large value items,
when there is a significant risk and other means do not provide sufficient appropriate audit
evidence, or when the repetitive nature of a calculation or other process performed
automatically by an information system makes a 100% examination cost effective, for
example, through the use of computer-assisted audit techniques (CAAT’s).
CAAT’s - Use of a computerised random number generator (through CAAT’s) or random
number tables.
Systematic selection - The number of sampling units in the population is divided by the
sample size to give a sampling interval, for example 50, and having determined a starting
point within the first 50, each 50th sampling unit thereafter is selected. Although the starting
point may be determined haphazardly, the sample is more likely to be truly random if it is
determined by use of a computerised random number generator or random number tables.
When using systematic selection, the engagement team would need to determine that
sampling units within the population are not structured in such a way that the sampling
interval corresponds with a particular pattern in the population.
Haphazard selection - In this, the sample is selected without following a structured
technique. Although no structured technique is used, one would nonetheless avoid any
conscious bias or predictability (for example, avoiding difficult to locate items, or always
choosing or avoiding the first or last entries on a page) and thus attempt to ensure that all
items in the population have a chance of selection. Haphazard selection is not appropriate
when using statistical sampling.
Block selection - This involves selecting a block(s) of contiguous items from within the
population. Block selection cannot ordinarily be used in audit sampling because most
populations are structured such that items in a sequence can be expected to have similar
characteristics to each other, but different characteristics from items elsewhere in the
population. Although in some circumstances it may be an appropriate audit procedure to
examine a block of items, it would rarely be an appropriate sample selection technique
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when one intends to draw valid inferences about the entire population based on the
sample. An example of where block selection can be used as a sample selection technique
is when testing for cut-offs.
Value weighted selection - In this method, the currency unit value rather than the physical
unit is used as the sampling population. Each individual monetary unit is given an equal
chance of selection (for example, one CU amount is selected out of the first ten thousand,
and thereafter, each ten thousandth CU amount is examined). As an individual currency
unit cannot be examined, the item which includes that CU amount is chosen for
examination. The chance of each item being selected is proportional to the value of the
item. A result of value-weighted selection is that high value items have a greater chance of
being selected, and confirmation regarding a large proportion of the population can often be
gained. Value weighted selection can also be simulated by stratifying the population and
selecting proportionately, more items from particular strata.
17.6.3 The decision as to which approach to use will depend on the circumstances, and the
application of any one or combination of the above means may be appropriate in particular
circumstances. While the decision as to which means, or combination of means to use is made
on the basis of the risk of material misstatement related to the assertion being tested and audit
efficiency, the engagement team needs to be satisfied that methods used are effective in
providing sufficient appropriate audit evidence to meet the objectives of the audit procedure.
17.7. Planning the sample
17.7.1 When planning the sample, the following need to be considered:
The audit objectives
17.7.2 In particular, this would be whether tests are for overstatement or understatement, or
whether it is a test of controls.
The population
17.7.3 The parameters of the population to be tested need to be determined. It must be
ensured that the population from which the sample is to be drawn is appropriate for the specific
audit objective (for example, if testing trade receivables, the population should be the trade
receivable balances listing; if testing trade payables, the population should be subsequent
disbursements, unpaid invoices and suppliers' statements). When defining a population, the
following should be remembered:
• The results of a test on a sample can only be evaluated to form a conclusion on the
population from which the sample is taken.
• Sampling from a population does not establish the completeness of that population.
• The extent of key or high value items must be considered.
• The different considerations that apply to debit or credit balances within an account
balance.
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• The extent to which a population can be divided into smaller populations, each of which is a
group of sampling units with similar characteristics (that is, how stratified the population can
be).
The sampling unit
17.7.4 The sampling unit needs to be defined, as the selection of the sample and the
evaluation of the test results depend on the unit selected. The value of all units must equal the
total value of the account balance or class of transactions. Often, the population can be divided
into sampling units in a variety of ways (for example, in trade receivables balances
confirmation requests, customer balances, individual invoices or items on invoices can be
used).
The definition of error
17.7.5 There must be a clear definition of what constitutes an error (such as a departure from a
control or an error in monetary terms) and to define it, there must be a clear understanding of
the objective of the test. For example, the purpose of examining trade receivables is to
determine whether individual balances in the total trade receivables account are actually owed
to the entity, and to assist in determining whether sales cut-off has been performed
satisfactorily. The purpose of the test is not to determine whether the debtor will be able to pay
(although information may be received during the confirmation procedure that will indicate
this).
Defining tolerable and expected error
17.7.6 Having defined an error, consideration must be given as to what the tolerable error will
be. Tolerable error has been defined above and is considered at the planning stage and, for
substantive procedures, is related to judgement on materiality. The smaller the tolerable error,
the greater the sample size will need to be. All errors found should be recorded in form 335 -
summary of unadjusted errors set out in part I of the manual.
Stratification
17.7.7 By stratifying, focus can be placed on items which are especially significant or most
vulnerable to material error. The strata need to be explicitly defined so that each sampling unit
can belong to only one stratum.
17.8. Setting the sample size for substantive tests of transactions and balances
17.8.1 The auditor shall determine a sample size sufficient to reduce sampling risk to an
acceptably low level. The lower the sampling risk that the team is willing to accept, the greater
the sample size will be.
17.8.2 The following factors are particularly important when setting a sample size:
The sampling risk.
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The tolerable error rate.
The expected error rate.
The population value (substantive tests of account balances only).
The number of items in the population (small populations only).
17.8.3 Sampling risk will always be present if a sample is tested within a population, rather
than the entire population being tested. The key factor is to decide the level of sampling risk to
accept. This decision is influenced by the amount of reliance being placed on the test.
Reliance on the test will be low if:
The population tested is inherently unlikely to contain errors;
Reliance is being placed on analytical procedures;
Reliance is being placed on internal controls; or
Reliance is being placed on other substantive procedures.
17.8.4 The lower the risk the engagement team is willing to accept, the greater the sample size
will need to be. Therefore, the level of sampling risk to accept will be decided by the
assessment of inherent, analytical and control risk.
17.8.5 Sampling risk can be contrasted with non-sampling risk which occurs during any audit
procedures, since most evidence is persuasive rather than conclusive. The auditor may use
inappropriate procedures or misinterpret evidence and fail to recognise an error. Attempts can
be made to reduce non-sampling risk by appropriate planning, supervision and review
procedures.
17.8.6 The most common method for setting sample sizes using statistical methods is shown
below. This method is the benchmark treatment of HLB International. In this example on trade
receivables, the total sample size would determine the number of items to select for testing
sales transactions and the number of debtor balances to select for testing:
Value of the population to be audited (e.g. receivables) CU
Individual area materiality level (Form 420 in Part I of the Manual) 2,000,000
Risk factor (Based on statistical tables)
Sampling Interval (materiality level / risk factor) 200,000
2.0
100,000
17.8.7 The individual area materiality level (or the tolerable error) is generally used unless the
engagement team feels a different level of tolerable error is required.
17.8.8 Total of higher value items greater than the planned sampling interval i.e. the total value
of individual items which exceed the $100,000 sampling interval - Say 2 items totalling
$600,000.
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If the test is for overstatement (e.g. trade receivables)
Total population CU
Less: Higher value items (to be tested 100%) 2,000,000
Total of lower value items (600,000)
Divided by: planned sampling interval 1,400,000
Sample size for lower value items
Add: Sample size for higher value items 100,000
Total sample size 14
2
16
If the test is for understatement (e.g. sales), higher value items would not automatically be
tested, as the size of the potential error would not be proportional to the size of the item.
Therefore, the sample size in the example above would be:
Total population CU
Divided by: planned sampling interval 2,000,000
Total sample size
100,000
20
Performing audit procedures
17.8.9 The engagement team should perform audit procedures appropriate to the particular
audit purpose on each item selected.
17.8.10 If a selected item is not appropriate for the application of the audit procedure, the audit
procedure is ordinarily performed on a replacement item. For example, a cancelled delivery
note may be selected when testing operating effectiveness of controls over sales. If the
engagement team is satisfied that the delivery note has been properly cancelled such that it
does not constitute an error, an appropriately chosen replacement can be examined.
17.8.11 In certain instances, the engagement team may be unable to apply the designed audit
procedures to a selected item because, for instance, documentation relating to that item has
been lost. If suitable alternative audit procedures cannot be performed on that item, the
engagement team ordinarily considers that item to be in error.
17.8.12 An example of a suitable alternative audit procedure might be the examination of
subsequent receipts when no reply has been received in response to a positive confirmation
request.
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Determining Sample Sizes
17.9 Test of Controls – attribute sampling
17.9.1 The sample size for test of controls can be determined non-statistically or statistically.
Non-statistical
17.9.2 None, few or many items can be selected based on the judgment of the auditor. The
following factor will influence the auditor’s judgment of the size of the sample.
This Change In This Factor Causes This Change in
the Sample Size:
Increase Tolerable rate
Decrease Risk of assessing control Decrease
Increase Increase
Decrease risk too low Decrease
Increase Expected population Increase
Decrease Increase
deviation rate Decrease
Increase or decrease Population size No effect, unless the
population is very small
17.9.3 When you have a small population and infrequent operating controls use the following
table as a guide - the table below is from the American Institute of Certified Public Accountants
Audit Sampling Guide.
Frequency of Control Sample Size
Quarterly 2
Monthly 2-4
3-8
Semi monthly 5-9
Weekly
Statistical
79.4 When it comes to larger populations (high frequency on transactions), many auditors use
statistical sampling.
17.9.5 The following tables are from the American Institute of Certified Public Accountants
Audit Sampling Guide. They are computed using binomial distributions.
17.9.6 Table A.1 and A.2 – These tables use risks of over reliance of 5 and 10%, respectively
(or 95% and 90% confidence intervals).
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17.9.7 In practice, the auditor can follow these methods of selecting a sample size.
Hold the tolerable rate at constant (for example at 10%) and then vary the risk of over
reliance depending on the desired level of assurance. For example if there are no
complementary monitoring or redundant controls, the auditor may desire to get to a high
level of assurance from the control and therefore would require 5% risk of overreliance (or
95% confidence interval). When less assurance is desired, a much higher level of risk of
overreliance is used. (For example 10%, 20%, 30% or over 40%).
Hold the level of assurance for all test of controls at a constant (for example, the risk of
over reliance at 5% or confidence interval at 95%) and for each separate test, select a
desired tolerable rate. In assessing the tolerable rate, the auditor should note that a
deviation from a pertinent control increases the risk of material misstatement, however,
such deviations do not always result in misstatements. For this reason, auditors usually
assess a tolerable rate for the test of controls that is higher than the comparable tolerable
rate of dollar deviation (performance materiality for the account balance).
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17.9.8 Below are the resulting sample sizes, when the expected deviation rate is zero
(discovery sampling) and the tolerable deviations of 5% and 10% for various confidence levels.
Table B.1
% Tolerable Confidence Resulting % Tolerable Confidence Resulting
Deviation Sample Deviation Sample
Rate Level Size Rate Level Size
5% 95 59 10% 95 29
5% 90 45 10% 90 22
5% 85 37 10% 85 18
5% 80 32 10% 80 16
5% 75 28 10% 75 14
5% 70 24 10% 70 12
5% 65 21 10% 65 10
5% 60 18 10% 60 9
5% 55 16 10% 55 8
5% 50 14 10% 50 7
5% 45 12 10% 45 6
5% 40 10 10% 40 5
5% 35 9 10% 35 5
5% 30 7 10% 30 4
5% 25 6 10% 25 3
5% 20 5 10% 20 3
17.9.9 An auditor should use a lower tolerable deviation rate for very critical controls. A
deviation on a control does not necessarily result in a misstatement of an account balance;
therefore, it is difficult to make a direct connection between a control deviation and a
misstatement of an account balance.
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17.9.10 The table B.-1 assumes that the number of deviations for the controls testing is zero; if
you have deviations use the tables below to get the appropriate sample sizes for a given
confidence level. See table C.1 and C.2 below:
17.9.11 Table C.1
Sample size Sample size Sample size
% Tolerable Confidence with zero with one with two
Deviation Level
Rate 95 deviations deviation deviations
90
5% 85 59 95 124
5% 80
5% 75 45 77 105
5% 70
5% 65 37 67 94
5% 60
5% 55 32 59 85
5% 50
5% 45 28 53 78
5% 40
5% 35 24 49 72
5% 30
5% 21 44 67
5%
18 40 62
16 37 58
14 34 54
12 31 50
10 28 46
9 25 42
7 22 39
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17.9.12 Table C.2
Sample size Sample size Sample size
% Tolerable Confidence with zero with one with two
Deviation
Rate Level deviations deviation deviations
10% 95 29 46 61
10% 90 22 38 52
10% 85 18 33 46
10% 80 16 29 42
10% 75 14 27 39
10% 70 12 24 36
10% 65 10 22 33
10% 60 9 20 31
10% 55 8 19 29
10% 50 7 17 27
10% 45 6 15 25
10% 40 5 14 23
10% 35 5 13 21
10% 30 4 11 20
17.9.13 The guide to Using International Standards on Auditing in the Audits of Small-and
Medium-Sized entities has the following table that can be used as a general guide for those
auditors who do not have access to statistical tables and those who do not wish to quantify
their sampling risks.
Tolerable # of Sample Size
Errors
10 30 60
0 MR LR LR
1
2 MR LR
MR
MR - Moderate Risk, LR -
Low Risk
17.9.14 In general, when the operating effectiveness of an internal control is assessed at high,
moderate or low, the following confidence intervals are used. However, these guidelines are
not a substitute for sound auditor judgments. Many other surrounding circumstances, such as
redundant controls, observation and inquiries, affect the auditor’s judgment on this
assessment.
Low risk is 90% confidence interval (10% risk of over reliance) and higher
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Moderate risk is 89% to 75% confidence interval (11% to 25% risk of over
reliance)
High risk is confidence intervals less than 74% (26% or higher risk over reliance)
to no controls testing
At a 10% tolerable deviation rate, this would mean that you would test (see table B.1)
For Low Control Risk – 22 items and for moderate Control Risk- 16 items.
At a 5% tolerable deviation rate, this would mean that you would test (see table B.1)
For Low Control Risk – 45 items and for moderate Control Risk- 32 items.
Test of Details (or Substantive Procedures)
17.9.15 Using the audit risk model and targeting 5% or lower as an acceptable level of audit
risk, the auditor should determine a sample size. (Audit Risk=Risk of Material Misstatement *
Analytical Review Procedures * Test of Details).
Non Statistical
17.9.16 The non-statistical sampling tables are based on statistical sampling concepts and
tables. Before determining the size, the auditor should assess the inherent risk and control
risks of the account assertion. (Without testing the operating effectiveness of the control it can
be difficult to justify lowering the control risks below maximum).
Inherent Control Confidence Factor (a)
Risk Risk
Maximum Maximum Without effective Analytical
Maximum Moderate Review Procedures (b)
Maximum Low 3.0
Moderate Maximum 1.5
Moderate Moderate 1.0
Moderate Low 1.7
Low Maximum 1.1
Low Moderate 0.8
Low Low 1.3
0.9
0.6
Confidence factors are from the Poisson distribution table.
With effective analytical review procedures, the auditor should be able to reduce the
number of items in the sample by reducing the confidence factor by approximately 20 to
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25%. Effective analytical review procedures are those that are Persuasive and/or
Corroborative.
17.9.17 Sample Size Determination
Sample size: (1)
Performance Materiality (2)
Monetary value of population (3)
Specific items subject to separate evaluation (a) (4)
Monetary total subject to sampling: (2)-(3) (5)
Expected errors in the sample (b) (6)
Sampling Interval (1)-(5) (7)
Confidence Factor (8)
Sample size: (4) x (7) divided by (6)
Specific items can be high valued items, negative items or others. These specific items
could be tested separately and at 100%. High value threshold is computed as the
performance materiality (Tolerable Misstatement) divided by a factor. (This factor can
be the assurance factor or 1/2, 1/3 or 1/6 or a factor that the auditor determines based
on his or her judgment of the various risks and population characteristics).
The expected errors in the sample should be multiplied by a factor of 1.7 (if high
combined risk), 1.5 (if moderate combined risk) or 1.2 (low combined risk).
Alternatively, the auditor can use the following table to obtain the confidence factor for
a given ratio of expected error to performance materiality.
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17.9.18 The following table is based on the Poisson distribution and it is obtained from the
American Institute of Certified Public Accountants Audit Sample Guide.
Ratio of 5% 10% Risk of Incorrect Acceptance 35% 50%
Expected 15% 20% 25% 30%
Errors 1.05 0.70
1.12 0.73
to Tolerable 1.20 0.77
Misstatement 1.28 0.82
1.38 0.87
0.00 3.00 2.31 1.90 1.61 1.39 1.21 1.50 0.92
1.63 0.99
0.05 3.31 2.52 2.06 1.74 1.49 1.29 1.79 1.06
1.99 1.14
0.10 3.68 2.77 2.25 1.89 1.61 1.39 2.22 1.25
2.51 1.37
0.15 4.11 3.07 2.47 2.06 1.74 1.49 2.89 1.52
3.38 1.70
0.20 4.63 3.41 2.73 2.26 1.90 1.62
0.25 5.24 3.83 3.04 2.49 2.09 1.76
0.30 6.00 4.33 3.41 2.77 2.30 1.93
0.35 6.92 4.95 3.86 3.12 2.57 2.14
0.40 8.09 5.72 4.42 3.54 2.89 2.39
0.45 9.59 6.71 5.13 4.07 3.29 2.70
0.50 11.54 7.99 6.04 4.75 3.80 3.08
0.55 14.18 9.70 7.26 5.64 4.47 3.58
0.60 17.85 12.07 8.93 6.86 5.37 4.25
Note: If the non-statistical sample design is planned without stratification, the auditor must
increase the sample size obtained from the above table. If there is variability in the population,
the auditor should stratify the sample – higher percentage (66%) on the sample should come
from high dollar items and a lower percentage (34%) from lower dollar items. If the auditor
does not stratify the sample then the sample size should be increased by 20% to 50%
depending on the variability of the population.
Statistical
17.9.19 While there are a few different statistical sampling methods, the method described
below is Monetary Unit Sampling. One of the main differences in the non-statistical method
described before and this statistical method is the selection of the sample items. Sample items
are selected on a probability proportional to size (PPS). By performing a PPS sample
selection the auditor does not have to stratify the sample and that will help the auditor reduce
the size of the sample. Many computer aided tools can be used to perform a PPS sample
selection.
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Inherent Control Risk Confidence Level - Without
Risk Maximum Effective Analytical
Maximum Moderate Procedures
Maximum Low 90% - 95%
Maximum Maximum 75%
Moderate Moderate 60%
Moderate Low 80%
Moderate Maximum 65%
Low Moderate 50%
Low Low 70%
Low 55%
40%
17.9.20 With effective analytical review procedures, the auditor should be able to reduce
the number of items in the sample by reducing the confidence level by approximately 10 to
15%. Effective analytical review procedures are those that are Persuasive and/or
Corroborative.
17.9.21 When determining the sample size, the auditor should use the same table as the
one used for non-statistical sampling by matching the confidence level to the
confidence factor in the chart below:
Confidence Confidence
Level % Factor
98 3.7
95 3.0
94 2.82
93 2.66
92 2.53
91 2.41
90 2.31
85 1.9
80 1.61
75 1.39
70 1.21
65 1.05
60 0.92
55 0.8
50 0.7
45 0.6
40 0.52
30 0.36
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17.9.22 The following examples are from the Guide to Using International Standards on
Auditing in the Audits of Small- and Medium-Sized Entities
Example 1 - Sampling Accounts Receivable Balances
Question Response
To ensure the existence of accounts receivable by
Purpose of test selecting a sample of receivable balances and sending
Risks of material misstatement in confirmation letters.
the relevant assertions
Population to be tested Existence = high risk
Monetary value of population Accounts receivable balances at year-end.
Specific items subject to separate 177203 €
evaluation
Assurance obtained from testing 38,340 €
controls
Assurance from other procedures None.
such as risk assessment
procedures Limited.
Confidence factor to be used No other sources of assurance so 95% or 3.0 will be
(reduce for assurance gained from used.
other sources) 15,000 €
Materiality None.
Expected deviations in sample
Sampling Interval = 15,000 €/3.0 = 5,000 €
Sample Size = (177,203 € - 38,340 €)/5,000 € = 28
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Example 2 - Sampling Accounts Receivable Balances
Question Response
To ensure the existence of accounts receivable by selecting
Purpose of test a sample of receivable balances and sending confirmation
Risks of material misstatement in letters.
the relevant assertions
Population to be tested Existence = moderate risk
Monetary value of population Accounts receivable balances at year-end.
Specific items subject to separate 177,203 €
evaluation
Assurance obtained from testing 38,340 €
controls A low level of control risk has been established over related
Assurance from other procedures controls.
such as risk assessment
procedures Limited.
Confidence factor to be used In light of other sources of evidence, a confidence factor of
(reduce for assurance gained from 70% (1.2) will be used.
other sources) 15,000 €
Materiality None.
Expected deviations in sample
Sampling Interval = 15,000 €/ 1.2 = 12,500 €
Sample Size = (177,203 € - 38,340 €)/12,500 € = 12
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Example 3 - Sampling Accounts receivable
Question Response
To ensure the existence of accounts receivable by
Purpose of test selecting a sample of receivable balances and sending
Risks of material misstatement in confirmation letters.
the relevant assertions Existence = Low risk
Population to be tested Accuracy = Low risk
Monetary value of population Purchase invoices for year
Specific items subject to separate 879,933 €
evaluation
Assurance obtained from testing 46,876 €
controls
Assurance from other procedures None.
such as risk assessment
procedures Moderately effective substantive analytical procedures.
Confidence factor to be used In light of the other sources of evidence, a confidence
(reduce for assurance gained from factor of 80% (1.6) will be used.
other sources) 15,000 €
Materiality None.
Expected deviations in sample
Sampling Interval = 15,000 €/1.6 = 9,375 €
Sample Size = (879,933 € - 46,876 €)/9,375 € = 89
As illustrated above, the sample sizes for substantive tests can become very large when
examining transaction streams. It is often more efficient to test internal control (where the
sample size is smaller) or perform other types of audit procedures to obtain the required
evidence.
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17.10 Nature and cause of errors
17.10.1 The engagement team shall consider the sample results, and investigate the nature
and cause of any errors identified, and their possible effect on the particular audit objective and
on other areas of the audit.
17.10.2 In analysing any errors discovered, the engagement team may observe that many
have a common feature, for example, type of transaction, location, and product line or period
of time. In such circumstances, the engagement team may decide to identify all items in the
population that possess the common feature and extend audit procedures in that stratum. In
addition, such errors may be intentional and may indicate the possibility of fraud.
17.10.3 In extremely rare circumstances, the engagement team may be able to establish that
an error is an anomalous error. For an error to be considered an anomalous error, the
engagement team has to have a high degree of certainty that such error is not representative
of the population. This certainty can be obtained by performing additional audit procedures,
which would depend on the situation, but should be adequate to provide the engagement team
with sufficient appropriate audit evidence that the error does not affect the remaining part of
the population. An example of such an error is an error caused by use of an incorrect formula
in calculating all inventory values at one particular branch. To establish that this is an
anomalous error, the engagement team will need to ensure that the correct formula has been
used at other branches.
Projecting errors
17.10.4 For tests of details, the engagement team should project monetary errors found in the
sample to the population, and should consider the effect of the projected error on the particular
audit objective and on other areas of the audit. The engagement team should project the total
error for the population to obtain a broad view of the scale of errors, and to compare this to the
tolerable error. For tests of details, tolerable error is the tolerable misstatement, and will be an
amount less than or equal to the materiality used for the individual class of transactions or
account balances being audited.
17.10.5 When an error has been established as an anomalous error, it may be excluded when
projecting sample errors to the population. The effect of any such error, if uncorrected, still
needs to be considered in addition to the projection of the non-anomalous errors. If a class of
transactions or account balance has been divided into strata, the misstatement is projected for
each stratum separately. Projected misstatements for each stratum plus anomalous errors are
then combined when considering the possible effect of misstatements on the total class of
transactions or current balance.
17.10.6 For tests of controls, no explicit projection of errors is necessary since the sample
error rate is also the projected rate of error for the population as a whole. ISA 330 provides
guidance when errors from controls upon which the auditor intends to rely are detected.
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17.11. Evaluating the results of audit sampling
17.11.1 The auditor shall evaluate:
The results of the sample; and
Whether the use of audit sampling has provided a reasonable basis for conclusions about
the population that has been tested.
17.11.2 For tests of controls, an unexpectedly high sample deviation rate may lead to an
increase in the assessed risk of material misstatement, unless further audit evidence
substantiating the initial assessment is obtained. For tests of details, an unexpectedly high
misstatement amount in a sample may cause the auditor to believe that a class of transactions
or account balance is materially misstated, in the absence of further audit evidence that no
material misstatement exists.
17.11.3 In the case of tests of details, the projected misstatement plus anomalous
misstatement, if any, exceeds tolerable misstatement, the sample does not provide a
reasonable basis for conclusions about the population that has been tested. The closer the
projected misstatement plus anomalous misstatement is to tolerable misstatement, the more
likely that actual misstatement in the population may exceed tolerable misstatement. Also, if
the projected misstatement is greater than the auditor’s expectations of misstatement used to
determine the sample size, the auditor may conclude that there is an unacceptable sampling
risk that the actual misstatement in the population exceeds the tolerable misstatement.
Considering the results of other audit procedures helps the auditor to assess the risk that
actual misstatement in the population exceeds tolerable misstatement, and the risk may be
reduced if additional audit evidence is obtained.
17.11.4 The following steps should be taken:
Analyse the effect of errors found.
Project the error or deviations found.
Assess the risk of an incorrect conclusion.
17.12. Analysing the effect of errors found
Assess whether any problems found fulfil the definition of error
17.12.1 This will involve checking if there are differences between the recorded amount and
the amount deemed to be correct, to assess whether there is in fact an error e.g. in a
substantive test relating to the recording of receivables. A mis-posting between customer
accounts does not affect the total debtor balance and it may be inappropriate to consider this
an error in this particular test, even though it may affect other audit areas.
Analyse the causes and nature of errors or deviations
17.12.2 Consider whether errors were unintentional or deliberate, whether due to a specific
event (such as the absence of key staff) or whether capable of occurring throughout the year.
If it can be concluded that the error was an isolated, non-recurring one, it can be excluded
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when evaluating errors in the remainder of the population. The estimated amount of that error
will be its actual amount.
Carry out further tests
17.12.3 It may be discovered that errors found share a common feature, in which case testing
may be extended on items that possess that feature. In addition, other audit procedures could
mitigate the effect of an error found e.g. if a non-reply to a receivable balance confirmation
request was deemed an error, review of after-date cash from the customer would mitigate that
error. Alternatively, errors detected may impact on other areas of the audit.
17.13. Projection of errors or deviations
17.13.1 The engagement team should estimate the most likely level of error in the population
(the potential error) by projecting the results of the sample. This is done to enable the
comparison of the potential error with the tolerable error. This does not, however, imply that
the precise rate of error can be quantified. Two methods are often used when projecting errors,
the ratio method and the difference method.
The ratio method
17.13.2 The ratio method should be used when the amount of error in an item relates to the
size of the item. As the monetary value of the item increases, so does the monetary value of
the error. The projected error is found by the formula:
Potential Error = Error found x Value of the population
in sample Value of the sample
17.13.3 To this should be added the actual error found in any items which have not been
subject to sampling, that is, items tested in full.
For example: $
2,000,000
Total value of population 800,000
Items tested 100% 400,000
Value of sample tested
Errors found in items 15,000
Tested 100% 9,000
Errors found in sample tested
Potential error in sample: 9000 x 2,000,000 - 800,000
Add: Actual error in items 100% tested = 400,000
27,000
15,000
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Potential Error in the Population 42,000
The difference method
---------
17.13.4 This should be used where the error does not have a direct relationship to the
monetary value of the item, that is, the error is relatively constant for all items, and so will
increase in proportion to the number of items in the population.
The potential error is found by the formula:
Potential Error in sample = Error found x Number of items in the population
in sample Number of items in the sample
To this is added the actual error in items tested 100%.
For example:
Total number of items in the population 805
Number of items tested 100% 5
Number of items tested in the sample 40
Errors found in items 100% tested 15,000
Errors found in sample 9,000
Potential error sample = 805 - 5 x 9,000
40 = 180,000
Add: Actual error in items 100% tested 15,000
-----------
Potential Error in the Population 195,000
-----------
17.13.5 It may be useful to compute both projections, and consider the likelihood of the larger
being correct. Neither method will give worthwhile results if the error rate is low.
17.13.6 If an unexpectedly high sample error rate is established during the testing of controls,
it may lead the engagement team to increase the assessed risk of material misstatement,
unless further audit evidence substantiating the initial assessment is obtained. In the case of
tests of details, an unexpectedly high error amount in a sample may lead the engagement
team to assess that a class of transactions or account balance is materially misstated, if further
audit evidence to the contrary does not exist.
17.13.7 If the total amount of projected error plus anomalous error is less than but close to the
tolerable error, the engagement team should consider the persuasiveness of the sample
results in the light of other audit procedures, and may consider it appropriate to obtain
additional audit evidence.
17.13.8 When using statistical sampling, the engagement team could compute a confidence
interval indicating the likely range of possible error or deviation. One suggested way of doing
this is to compare an upper error limit with the tolerable error as follows, but a less complicated
approach can be adopted.
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17.13.9 Using the figures from the example above, assume that the sampling interval is
$10,000 and the actual errors found were as follows, where the lower value errors can be
expressed as a proportion of book value of items in error.
Error Book value Amount of Amount of Lower value Lower value
1 of item in over / (under) high value errors errors
2 error statement items in error Over Under
3 30,000 (10,000) (10,000) - -
4 30,000 25,000 25,000 - -
5 40,000 8,000 - 0.2 -
20,000 (2,000) - - 0.1
10,000 3,000 - 0.3 -
15,000 0.5 0.1
17.13.10 The upper error limit therefore comprises:
The net total errors in high value items ($15,000).
The most likely error of understatement in lower value items, which is the sampling interval
multiplied by the proportion of book value understated in the sample ($10,000 x 0.1=
$1,000).
The most likely error of overstatement in lower value items. This is calculated by
multiplying the sampling interval by an adjusted risk factor for the area audited, adjusted
for the number of errors (or proportion of book value overstated in the sample = 0.5).
17.13.11 Assuming an original risk factor of 2.2 and using the table of adjusted risk factors
provided in statistical sampling - Table of error adjusted risk factors of this section, for two
errors of overstatement discovered in lower value items, the adjusted risk factor is 2.2 + (0.5 x
5.19) = 4.79
= high value errors 15,000
most likely error of understatement in lower value items (1,000)
maximum error of overstatement in lower value items (4.79 x 10,000) 47,900
-----------
Upper Error Limit 61,900
-----------
17.13.12 Therefore, if the upper error limit does not exceed the tolerable error, the book value
can be accepted. If it does exceed the tolerable error, the likelihood that the actual error
exceeds the tolerable error must be considered.
17.13.13 If there remains a concern about the upper error limit exceeding the tolerable error,
the management should be asked to adjust the specific errors to bring the maximum limit
below the tolerable error:
Request management to investigate identified errors and the potential for further errors,
and to make any necessary adjustments; and / or
Modify the nature, timing and extent of further audit procedures. For example, in the case
of tests of controls, the engagement team might extend the sample size, test an alternative
control or modify related substantive procedures; and / or
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Consider the effect on the audit report.
17.13.14 If any adjustment made does not bring the error below the tolerable error, the
following can be performed:
Reassess the nature of the errors found - this may show items of a certain nature are
subject to error and it may be possible to concentrate on these and accept other items.
Perform alternate audit procedures addressing the same management assertions.
Reconsider the level of the tolerable error (again, this needs to be justified).
Increase the sample size.
Consider the effect on the audit report.
17..13.15 Manual computations of upper limits can be time consuming. Many CAATS
(Computed Assisted Audit Techniques) can be used in the computations upper limits and
many other statistical sampling applications and results.
17.13.16 If non-statistical sampling is used, judgement has to be relied on in assessing the
likelihood that the actual error exceeds the tolerable error. This is done by comparing the
potential error to the tolerable error, and taking into account other relevant audit evidence and
reviewing the level of sampling risk the engagement team is prepared to accept. If the potential
error is greater than the tolerable error, the engagement team could conclude that insufficient
evidence has been obtained to conclude that the financial statements are fairly stated.
17.13.17 If the potential error is less than the tolerable error, the engagement team should
consider whether it would still obtain this result, if the true monetary error exceeded the
tolerable error.
17.13.18 If the engagement team is not able to obtain the required degree of assurance that
the actual error or deviation rate is less than the tolerable error the team should:
Request management to investigate misstatements that have been identified and the
potential for further misstatements to make any necessary adjustments; or
Tailor the nature, timing and extent of those further audit procedures to best achieve the
required assurance. For example, in the case of tests of controls, the auditor may extend
the sample size, test an alternative control or modify related substantive procedures.
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APPENDIX I: FACTORS INFLUENCING SAMPLE SIZE FOR TESTS OF CONTROLS
The following are factors that the auditor would consider when determining the sample size for
tests of controls. These factors, which need to be considered together, assume the
engagement team does not modify the nature or timing of tests of controls or otherwise modify
the approach to substantive procedures in response to assessed risks.
FACTOR EFFECT ON
SAMPLE SIZE
An increase in the extent to which the risk of material misstatement is
reduced by the operating effectiveness of controls. Increase
An increase in the rate of deviation from the prescribed control activity Decrease
that the auditor is willing to accept. Increase
An increase in the rate of deviation from the prescribed control activity Increase
that the auditor expects to find in the population.
An increase in the auditor’s required confidence level (or conversely, a Negligible
decrease in the risk that the auditor will conclude that the risk of material
misstatement is lower than the actual risk of material misstatement in the
population).
An increase in the number of sampling units in the population.
1. The extent to which the risk of material misstatement is reduced by the operating
effectiveness of controls. The more assurance the engagement team intends to obtain
from the operating effectiveness of controls, the lower its assessment of the risk of
material misstatement will be, and the larger the sample size will need to be. When the
engagement team’s assessment of the risk of material misstatement at the assertion level
includes an expectation of the operating effectiveness of controls, the team is required to
perform tests of controls. Other things being equal, the more the engagement team relies
on the operating effectiveness of controls in the risk assessment, the greater is the extent
of its tests of controls (and therefore, the sample size is increased).
2. The rate of deviation from the prescribed control activity the engagement team is willing to
accept (tolerable error). The lower the rate of deviation that the engagement team is
willing to accept, the larger the sample size needs to be.
3. The rate of deviation from the prescribed control activity the engagement team expects to
find in the population (expected error). The higher the rate of deviation that the
engagement team expects, the larger the sample size needs to be so as to be in a
position to make a reasonable estimate of the actual rate of deviation. Factors relevant to
the engagement team’s consideration of the expected error rate include the team’s
understanding of the business (in particular, risk assessment procedures undertaken to
obtain an understanding of internal control), changes in personnel or in internal control,
the results of audit procedures applied in prior periods and the results of other audit
procedures. High expected error rates ordinarily warrant little, if any, reduction of the
assessed risk of material misstatement.
4. The engagement team’s required confidence level. The greater the degree of confidence
that the engagement team requires that the results of the sample are in fact indicative of
the actual incidence of error in the population, the larger the sample size needs to be.
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5. The number of sampling units in the population. For large populations, the actual size of
the population has little, if any, effect on sample size. For small populations however,
audit sampling is often not as efficient as alternative means of obtaining sufficient
appropriate audit evidence.
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APPENDIX II: FACTORS INFLUENCING SAMPLE SIZE FOR SUBSTANTIVE TESTS
The following are factors that the auditor would consider when determining the sample size for
tests of details. These factors, which need to be considered together, assume the engagement
team does not modify the approach to tests of controls or otherwise modify the nature or timing
of substantive procedures in response to the assessed risks.
.
FACTOR EFFECT ON
SAMPLE SIZE
An increase in the auditor’s assessment of the risk of material Increase
misstatement.
An increase in the use of other substantive procedures directed at the Decrease
same assertion.
An increase in the auditor’s required confidence level (or conversely, a Increase
decrease in the risk that the auditor will conclude that a material error
does not exist, when in fact it does exist).
An increase in the tolerable error that the auditor is willing to accept. Decrease
An increase in the amount of error the auditor expects to find in the Increase
population.
Stratification of the population when appropriate. Decrease
The number of sampling units in the population. Negligible
1. The engagement team’s assessment of the risk of material misstatement. The higher this
is, the larger the sample size would need to be. The engagement team’s assessment of
the risk of material misstatement is affected by inherent risk and control risk. For example,
if the engagement team does not perform tests of controls, the team’s risk assessment
cannot be reduced for the effective operation of internal controls with respect to the
particular assertion. Therefore, in order to reduce audit risk to an acceptably low level, the
engagement team needs a low detection risk and will rely more on substantive
procedures. The more audit evidence that is obtained from tests of details (that is, the
lower the detection risk), the larger the sample size will need to be.
2. The use of other substantive procedures directed at the same assertion. The more the
engagement team is relying on other substantive procedures (tests of details or
substantive analytical procedures) to reduce to an acceptable level the detection risk
regarding a particular class of transactions or account balance, the less assurance it will
require from sampling and, therefore, the smaller the sample size can be.
3. The engagement team’s required confidence level. The greater the degree of confidence
that the engagement team requires that the results of the sample are in fact indicative of
the actual amount of error in the population, the larger the sample size needs to be.
4. The total error the engagement team is willing to accept (tolerable error). The lower the
total error that the auditor is willing to accept, the larger the sample size needs to be.
5. The amount of error the engagement team expects to find in the population (expected
error). The greater the amount of error the engagement team expects to find in the
population, the larger the sample size needs to be in order to make a reasonable estimate
of the actual amount of error in the population. Factors relevant to the engagement team’s
consideration of the expected error amount include the extent to which item values are
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determined subjectively, the results of risk assessment procedures, the results of tests of
control, the results of audit procedures applied in prior periods, and the results of other
substantive procedures.
6. Stratification. When there is a wide range (variability) in the monetary size of items in the
population, it may be useful to group items of similar size into separate sub-populations or
strata. This is referred to as stratification. When a population can be appropriately
stratified, the aggregate of the sample sizes from the strata generally will be less than the
sample size that would have been required to attain a given level of sampling risk, had
one sample been drawn from the whole population.
7. The number of sampling units. For larger populations, the actual size of the population
has little, if any effect on the sample size. Thus for small populations, audit sampling is
often not as efficient as alternative means of obtaining sufficient appropriate audit
evidence.
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18. AUDITING ACCOUNTING ESTIMATES, INCLUDING FAIR VALUE ACCOUNTING
ESTIMATES, AND RELATED DISCLOSURES (INCORPORATING ISA 540)
18.1. Objective
18.1.1 The objective of the auditor is to obtain sufficient appropriate audit evidence, in the
context of the applicable financial reporting framework, about whether:
Accounting estimates, including fair value accounting estimates, in the financial statements,
whether recognised or disclosed, are reasonable; and
Related disclosures in the financial statements are adequate.
18.2. Definitions
18.2.1 Accounting Estimate – An approximation of a monetary amount in the absence of a
precise means of measurement. This term is used for an amount measured at fair value where
there is estimation uncertainty, as well as for other amounts that require estimation. For
accounting estimates involving measurement at fair value, the term “fair value accounting
estimates” is used.
18.2.2 Auditor’s point estimate or auditor’s range – The amount, or range of amounts,
respectively, derived from audit evidence for use in evaluating management’s point estimate.
18.2.3 Estimation uncertainty – The susceptibility of an accounting estimate and related
disclosures to an inherent lack of precision in its measurement.
18.2.4 Management bias – A lack of neutrality by management in the preparation of
information.
18.2.5 Management’s Point Estimate – The amount selected by management for recognition
or disclosure in the financial statements as an accounting estimate.
18.2.6 Outcome of an accounting estimate – the actual monetary amount which results from
the resolution of the underlying transaction(s), event(s) or condition(s) addressed by the
accounting estimate.
18.3. Nature of accounting estimates
18.3.1 Some financial statement items cannot be measured precisely, but can only be
estimated. For the purposes of ISA 540, such financial statements items are referred to as
accounting estimates. The nature and reliability of information available to management to
support the making of an accounting estimate varies widely, which thereby affects the degree
of estimation uncertainty associated with accounting estimates. The degree of estimation
uncertainty affects, in turn, the risks of material misstatement of accounting estimates,
including their susceptibility to unintentional or intentional management bias.
18. Auditing accounting estimates 87 of 150
1st September 2015
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Audit Manual Part1
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Audit Manual Part1
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