Before sampling a set of data, you must calculate the statistically appropriate sample size, and other values required by the subsequent sample and evaluate operations. Show
The Calculate Sample Size feature in Analytics calculates the required values for you based on input values you provide. The importance of calculating a sample sizeCalculating an appropriate sample size is critical to the validity of the subsequent sample. If the sample is not valid, or representative, you cannot reliably project the results of audit procedures you perform on the sample to the entire population. Do not skip calculating a sample size, or guess at a sample size. Most of the input values you use to calculate sample size are based on your professional judgment. Ensure that you fully understand the implications of the values before relying on the results of sampling in a production environment. Consult audit sampling resources, or an audit sampling specialist, if you are in doubt. How input values affect sample sizeInput values affect the sample size calculated by Analytics. You can use the Calculate button in the Size dialog box to experiment with how the different input values affect the sample size. The table below summarizes the effect of input values on sample size. Caution In a production environment, do not manipulate input values solely to achieve a smaller sample size. Input values should be based on your professional judgment about what is most appropriate for the data being sampled and the audit objective.
StepsNote Do not include the thousands separator, or the percentage sign, when you specify values. These characters prevent the command from running, or cause errors.
Size dialog box inputs and resultsThe tables below provide detailed information about the input values and output results in the Size dialog box. Main tab – input values
Main tab – output results
An example of inputs and resultsCalculating the size of a monetary unit sample for the Invoices tableThe figure below provides an example of input values and output results when calculating sample size for monetary unit sampling.
The calculation is based on the Invoices table in ACL_Rockwood.acl (ACL DATA\Sample Data Files\ACL_Rockwood\ACL_Rockwood.acl). Maximum Tolerable Taintings (%)Note If you intend to use the evaluation feature in Analytics, you do not need to use the value reported by Maximum Tolerable Taintings (%). Instead, you use the Upper Error Limit calculated by the evaluation feature. For more information, see Evaluating errors in a monetary unit sample. Maximum Tolerable Taintings (%) provides one way of evaluating misstatement in a population. If you use this method, you know in advance the threshold value reported by Analytics, before you begin audit procedures on the sampled data. If cumulative errors you observe in the course of performing the procedures exceed the threshold value, you know at that point that the sample field is materially misstated. ExampleIn an accounts receivable table you discover that a book value of $1000 should actually be $930. In a misstated amount, tainting is the percentage of the book value that the misstatement represents.
After performing your substantive procedures on sampled data you can sum all the individual tainting percentages from any misstated amounts. If the sum of the tainting percentages is less than or equal to the Maximum Tolerable Taintings (%) reported by Analytics, you can consider that the amounts in the sample field as a whole are not materially misstated, for your specified confidence level. Example
You discover three misstated amounts in an accounts receivable table, which results in the following taintings, and total tainting percentage:
Let's assume the Maximum Tolerable Taintings (%) reported by Analytics when you calculated the sample size for the table was 92.30%. Because the total tainting percentage of 49% is less than 92.30%, you can conclude that the amounts in the sample field as a whole are not materially misstated, for your specified confidence level. Note Evaluation using Maximum Tolerable Taintings (%) is slightly more stringent than the evaluation feature in Analytics. If the sum of the tainting percentages marginally exceeds the Maximum Tolerable Taintings (%) value you should use the evaluation feature to confirm that the sample field is in fact materially misstated. For more information, see Evaluating errors in a monetary unit sample. Statistical validity of sample sizes generated by AnalyticsAnalytics generates statistically valid sample sizes for most analyses. Exceptions may apply in the following situations:
Poisson distribution versus binomial distributionTwo commonly used methods of generating sample sizes are the Poisson and the binomial distributions. Analytics generates sample sizes using the Poisson distribution. For typical data sets of a thousand or more records, the Poisson and the binomial distributions generate nearly identical sample sizes. For populations of under a thousand records, sample sizes determined with the Poisson distribution tend to be slightly larger and therefore more conservative than sizes determined with the binomial distribution. The binomial distribution adjusts the sample size downward for small populations but the Poisson distribution does not. With very small populations, the sample size generated by the Poisson distribution can actually exceed the population size. When calculating sample sizes in Analytics, recognize that for record sampling of small data sets, the sample size may be larger than you need. This larger sample size does not present an obstacle to analysis because it is common practice to manually oversample small populations. What is monetary unit sampling used for?Monetary unit sampling (MUS) is a statistical sampling method that is used to determine if the account balances or monetary amounts in a population contain any misstatements.
Which sample selection method is most appropriate for a statistical monetary unit sampling procedure?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.
How does monetary unit sampling mus ensure that larger dollar components are selected for examination?How does monetary unit sampling (MUS) ensure that larger dollar components are selected for examination? MUS sampling requires the auditor to stratify the sample into larger and smaller dollar components prior to beginning the sample selection process.
When using monetary unit sampling is the recorded Dollar population?When using monetary unit sampling, the recorded dollar population is a definition of all the items in the: population. When the sample selection is done using probability proportional to size sample selection (PPS): population items with a zero recorded balance have no chance of being selected.
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