How many types of audit sampling are there?

How many types of audit sampling are there?

ISA 530 Audit Sampling

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.

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.

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.

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

Applied through random number generators, for example, random number tables.

Systematic Selection

in which 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 computerized random number generator or random number tables.

When using systematic selection, the auditor 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.

Monetary Unit Sampling

It is a type of value-weighted selection in which sample size, selection and evaluation result in a

conclusion in monetary amounts.

Haphazard Selection

The auditor selects the sample without following a structured technique. Although no structured technique is used, the auditor 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

It involves the selection of 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 when the auditor intends to draw valid inferences about the entire population based on the sample.

Statistical vs Non-Statistical Sampling

The above sampling methods can be summarised into statistical and non-statistical sampling as follows:

Statistical sampling : Random sampling, Systematic sampling, Monetary unit sampling

Statistical sampling allows each sampling unit to stand an equal chance of selection.

Non-statistical sampling: Haphazard sampling, Block selection

The use of non-statistical sampling in audit sampling essentially removes this probability theory and is wholly dependent on the auditor’s judgment. Keeping the objective of sampling in mind, which is to provide a reasonable basis for the auditor to draw valid conclusions and ensuring that all samples are representative of their population, will avoid bias

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