Husky Inventory 2009 Table, Case Study Example

Find the sample sizes using attribute sampling with a 90% confidence level, an Upper Error Limit of 15%, and an Expected Error Rate of 5%.

Sample Size for Precision Percentage of Plus or Minus

How many tolerable errors are there for the selected sample?

The price value is the monetary amount of the products being sampled. In the above example, it would be Rs. 100,000,000.

3. Describe what happens to the sample size as you do the following,
4. keeping everything else the same as step (a):

If price value increases with all other factors remaining the same, materiality and planned precision become smaller percentages of the price value. Hence, the auditor would need a more precise estimate of the error in the cost. This would require a larger sample size.

All other factors remain the same, materiality and planned precision become larger percentages of the price value. Hence the auditor would not need to have as precise an estimate of the error in the cost. The auditor could then decrease the required sample size.

Increase the confidence level to 95%:

The expected aggregate error is subtracted from the materiality amount to arrive at planned precision. Increasing the expected aggregate error decreases planned precision, which increases the required sample size. Increasing the confidence level means that the auditor wants to be more certain about the results of his/her procedure, the auditor will need to take a larger sample to achieve this.

Increase the upper error limit to 25%

The same as decreasing the confidence level, the auditor is willing to be less certain about the results of his/her procedure, and can therefore take a smaller sample.

Increase the expected error rate to 10%

Materiality and planned precision become smaller percentages of the cost value. Hence, the auditor would need a more precise estimate of the error in the price. This would require a larger sample size.

Find the sample size using MUS sampling with a confidence level of 90%, materiality of 500,000, and expected total errors of 100,000.

Assuming there are no individually significant transactions that the auditor wants to examine (very large transactions or high risk transactions), the population value in this case is Rs. 1000,000, The materiality amount and the expected aggregate error are for the financial statements as a whole and therefore would be determined at the start of the planning process. For this example, we will use a Rs. 3,000,000 materiality amount and a Rs. 816,500 expected aggregate error.

The confidence level represents the amount of assurance that the auditor wishes to derive from the substantive test of details. The auditor normally determines this by using the audit risk model. Using that model, the confidence level is the converse of the substantive test of details risk (STDR) that results from the use of the audit risk model. For example, if STDR is determined to be 15%, the auditor will use an 85% confidence level for his/her sampling procedures.

While guidelines should not replace the use of professional judgment, the following may be useful:

Let’s assume that the auditor wants a very high level of assurance from the substantive test of details and selects a 95% confidence level. Using CAATS, the auditor would key in:

1. Confidence level (95);
2. Population value (100,000,000);
3. Materiality (3,000,000); and
4. Expected aggregate errors (816,500).

Assume the CAATS software will then produce a sample size of 181.

Net MLE                          =          MLE (over)  –  MLE (under)

Net UEL (over)                =          UEL (over)  –  MLE (under)

Net UEL (under)              =          UEL (under)  –  MLE (over)

In our example we have:

Net MLE                              =          MLE (over)  –  MLE (under)

=          Rs. 1,540,150  –  nil

=          Rs. 1,540,150.

Net UEL (over)                    =          UEL (over) – MLE (under)

=          Rs. 3,584,850  –  nil

=          Rs. 3,584,850.

Net UEL (under)                  =          UEL (under) – MLE (over)

=          Rs. 1,644,040 – Rs. 1,540,150

=         Rs. 103,890.