2016年ACCA考试《审计与认证业务》知识点(1)
来源 :中华考试网 2016-05-21
中Audit sampling
Paper F8, Audit and Assurance and Paper FAU, Foundations in Audit require
students to gain an understanding of audit sampling. While you won’t be
expected to pick a sample, you must have an understanding of how the various
sampling methods work. This article will consider the various sampling
methods in the context of Paper F8 and Paper FAU.
This subject is dealt with in ISA 530, Audit Sampling. The definition of audit
sampling is:
‘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.’1
In other words, the standard recognises that auditors will not ordinarily test all
the information available to them because this would be impractical as well as
uneconomical. Instead, the auditor will use sampling as an audit technique in
order to form their conclusions. It is important at the outset to understand that
some procedures that the auditor may adopt do not involve audit sampling,
100% testing of items within a population, for example. Auditors may deem
100% testing appropriate where there are a small number of high value items
that make up a population, or when there is a significant risk of material
misstatement and other audit procedures will not provide sufficient
appropriate audit evidence. However, candidates must appreciate that 100%
examination is highly unlikely in the case of tests of controls; such sampling is
more common for tests of detail (ie substantive testing).
The use of sampling is widely adopted in auditing because it offers the
opportunity for the auditor to obtain the minimum amount of audit evidence,
which is both sufficient and appropriate, in order to form valid conclusions on
the population. Audit sampling is also widely known to reduce the risk of
‘over-auditing’ in certain areas, and enables a much more efficient review of
the working papers at the review stage of the audit.
In devising their samples, auditors must ensure that the sample selected is
representative of the population. If the sample is not representative of the
population, the auditor will be unable to form a conclusion on the entire
population. For example, if the auditor tests only 20% of trade receivables for
existence at the reporting date by confirming after-date cash, this is hardly
representative of the population, whereas, say, 75% would be much more
representative.
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AUDIT SAMPLING
AUGUST 2011
SAMPLING RISK
Sampling risk is the risk that the auditor’s conclusions based on a sample may
be different from the conclusion if the entire population were the subject of the
same audit procedure.
ISA 530 recognises that sampling risk can lead to two types of erroneous
conclusion:
1. The auditor concludes that controls are operating effectively, when in
fact they are not. Insofar as substantive testing is concerned (which is
primarily used to test for material misstatement), the auditor may
conclude that a material misstatement does not exist, when in fact it
does. These erroneous conclusions will more than likely lead to an
incorrect opinion being formed by the auditor.
2. The auditor concludes that controls are not operating effectively, when in
fact they are. In terms of substantive testing, the auditor may conclude
that a material misstatement exists when, in fact, it does not. In contrast
to leading to an incorrect opinion, these errors of conclusion will lead to
additional work, which would otherwise be unnecessary leading to audit
inefficiency.
Non-sampling risk is the risk that the auditor forms the wrong conclusion,
which is unrelated to sampling risk. An example of such a situation would be
where the auditor adopts inappropriate audit procedures, or does not
recognise a control deviation.
METHODS OF SAMPLING
ISA 530 recognises that there are many methods of selecting a sample, but it
considers five principal methods of audit sampling as follows:
• random selection
• systematic selection
• monetary unit sampling
• haphazard selection, and
• block selection.
Random selection
This method of sampling ensures that all items within a population stand an
equal chance of selection by the use of random number tables or random
number generators. The sampling units could be physical items, such as sales
invoices or monetary units.
Systematic selection
The method divides the number of sampling units within a population into the
sample size to generate a sampling interval. The starting point for the sample
can be generated randomly, but ISA 530 recognises that it is more likely to be