Monetary unit sampling
Monetary unit sampling is a statistical sampling method for estimating the total amount of monetary misstatement in an account or class of transactions.
Monetary unit sampling works best with financial data that has the following characteristics:
|
no misstatements, or only a small number of misstatements For example, less than 5% of the items are misstated. |
| more likelihood of overstatements than understatements |
| no zero dollar items |
Monetary unit sampling is also known as:
- dollar-unit sampling
- probability-proportional-to-size sampling
Tip
For a hands-on introduction to the end-to-end process of monetary unit sampling in Analytics, see Monetary unit sampling tutorial.
How it works
Monetary unit sampling allows you to select and analyze a small subset of the records in an account, and based on the result estimate the total amount of monetary misstatement in the account.
You can then compare the estimated misstatement to the misstatement amount that you judge is material, and make a determination regarding the account.
Monetary unit sampling supports making this sort of statement:
- There is a 95% probability that the misstatement in the account balance does not exceed $28,702.70, which is less than the tolerable misstatement of $29,000.00. Therefore the amounts in the account are fairly stated.
Overview of the monetary unit sampling process
Caution
Do not skip calculating a valid sample size.
If you go straight to drawing a sample of records, and guess at a sample size, there is a high likelihood that the projection of your analysis results will be invalid, and your final conclusion flawed.
The monetary unit sampling process involves the following general steps:
- Calculate the required sample size
-
Choose a sample selection method:
-
Optionally specify one or more of the following options:
- Draw the sample of records
- Perform your intended audit procedures on the sampled data.
- Evaluate whether the observed levels of monetary misstatement in the sampled data represent an acceptable or unacceptable amount of misstatement in the account as a whole.
How monetary unit sampling selects records
Monetary unit sampling uses the following process for selecting sample records from an Analytics table:
- You specify a numeric field with monetary amounts as the basis for the sampling.
- The absolute value of all the amounts in the field is treated as a stream of monetary units, with each unit representing one cent ($0.01) of the absolute value.
-
Using one of the sample selection methods, Analytics selects samples from among the monetary units. The records corresponding to the selected monetary units are included in the sampling output table.
Example
A table contains an "Amount" field with the values shown below. The field has an absolute value of $11.75, and therefore contains 1,175 monetary units.
If the sampling process selects monetary units 399 and 1,007, records 2 and 5 are included in the output table. Records 1, 3, and 4 are not included.
| Record number | Amount | Cumulative balance (absolute) |
Monetary units | Unit selected by Analytics |
|---|---|---|---|---|
| 1 | $3.50 | $3.50 | 1 to 350 | |
| 2 | ($0.75) | $4.25 | 351 to 425 | 399 |
| 3 | $1.25 | $5.50 | 426 to 550 | |
| 4 | $0.75 | $6.25 | 551 to 625 | |
| 5 | ($5.50) | $11.75 | 626 to 1,175 | 1,007 |
A bias toward larger amounts
Monetary unit sampling intentionally creates a bias toward the selection of records containing larger amounts, whether positive or negative. Each monetary unit has an equal chance of selection, so a $1000 amount, which contains 100,000 monetary units, is four times more likely to be selected than a $250 amount, which contains 25,000 monetary units.
In other words, the probability that any given record will be selected is directly proportional to the size of the amount it contains.
Considerations
Monetary unit sampling is appropriate for use with substantive or misstatement testing. By biasing larger amounts, monetary unit sampling provides a high level of assurance that all significant amounts in a population are subject to testing. When testing for misstatement, it is the larger amounts that present the greatest risk of containing a material error.
If you choose a sampling method that biases large amounts, you may miss a potential problem related to small transactions. Problems with small transactions, once aggregated, may be material.
