In auditing, a sample should be:
a. Random - a random sample is one where each item of the population has an equal (or specified) chance of being selected. Statistical inferences may not be valid unless the sample is random.
b. Representative - the sample should be representative of the differing items in the whole population. For example, it should contain a similar proportion of high and low value items to the population (e.g. all the debtors).
c. Protective - protective, that is, of the auditor. More intensive auditing should occur on high value items known to be high risk.
d. Unpredictable - client should not be able to know or guess which items will be examined.
Sample Selection Methods:
There are several methods available to an auditor for selecting items. These include:
a. Haphazard -Simply choosing items subjectively but avoiding bias. Bias might come in by tendency to favor items in a particular location or in an accessible file or conversely in picking items because they appear unusual. This method is acceptable for non-statistical sampling but is insufficiently accurate for statistical sampling.
b. Simple random - All items in the population have (or are given) a number. Numbers are selected by a means which gives every number an equal chance of being selected. This is done using random number tables or computer or calculator generated random numbers.
c. Stratified - This means dividing the population into sub populations (strata = layers) and is useful when parts of the population have higher than normal risk (e.g. high value items, overseas debtors). Frequently high value items form a small part of the population and are 100% checked and the remainders are sampled.
d. Cluster sampling - This is useful when data is maintained in clusters (= groups or bunches) as wage records are kept in weeks or sales invoices in months. The idea is to select a cluster randomly and then to examine all the items in the cluster chosen. The problem with this method is that this sample may not be representative.
e. Random systematic - This method involves making a random start and then taking every nth item thereafter. This is a commonly use method which saves the work of computing random numbers. However the sample may not be representative as the population may have some serial properties.
f. Multi stage sampling - This method is appropriate when data is stored in two or more levels. For example stock in a retail chain of shops. The first stage is to randomly select a sample of shops and the second stage is to randomly select stock items from the chosen shops.
g. Block sampling - simply choosing at random one block of items e.g. all June invoices. This common sampling method has none of the desired characteristics and is not recommended.
h. Value weighted selection - This method uses the currency unit value rather than the items as the sampling population. It is now very popular and it is also known as “Monetary Unit Sampling”. This in relatively new variant of discovery sampling which is thought to have wide application in auditing.
This is because:
a. Its application is appropriate with large variance populations. Large variance populations are those like debtors or stocks where the members of the populations are of widely different sizes.
b. The method is suited to populations where errors are not expected.
c. It implicitly takes into account the auditor’s concept of materiality.
Procedures are:
a. Determine sample size. This will cover:
i. The size of the population
ii. The minimum unacceptable error rate (materiality)
iii. The Beta risk desired
b. List the items in the population (e.g. 1,250 debtors)
Debtors Name Balance Rs. Cumulative Rs.
Jameel 600 600
Ibrahim 100 700
Razi 1,200 1,900
Faiz 500 2,400
Saif 4,000 6,400
Etc. *** ***
Etc. *** ***
1,250. *** ***
_______ _______
300,000 300,000
====== ======
c. If the sample size were 100 items then take a random start say 1,000 and every 3,000th (Rs. 300,000/100 sample size) item thereafter, i.e. using systematic sampling with a random start.
The idea is that:
i. The population of debtors is not the 1,250 number of debtors but Rs. 300,000.
ii. If the particular Rupee is chosen then the whole balance of which that Re. 1 is a part will be investigated and any error quantified. In our example, Razi would be selected since 1,000 lies in his balance and then Saif would also be chosen as 1,000 + 3000 = 4,000 lies in his balance. Note that the larger balances have a greater chance of being selected. This is protective for the auditor but it has been pointed out that balances that contain errors of understatement will have reduced chance of detection.
d. At the end of the process, evaluate the result which might be a conclusion that the auditor is 95% confident that the debtors are not overstated by more than Rs. ***. Where Rs. *** is the materiality factor (tolerable error) chosen. If the conclusion is that the auditor finds that the debtors appear to be overstated by more than Rs. *** then he may take a larger sample and/or investigate the debtors more fully.
Monetary unit sampling is especially useful in testing for overstatement where significant understatements are not expected. Examples of applications include debtors, fixed assets and stocks. It is clearly not suitable for testing creditors where understatement is the primary characteristic to be tested.
