About evaluating sampling errors

Evaluating errors determines the impact of sampling errors on your data. The parameters used to draw the sample and any errors that were found in the sample, are used to calculate the upper error limit for the data set. In evaluating sampling errors, ACL uses the upper error limit cumulative factors of the Poisson distribution.

In record sampling, the upper error limit frequency is based on the number of errors, not the monetary value of the errors. The upper error limit is the maximum rate of error that is acceptable in the data set without detection, and is based on the number of errors and the specified confidence level. For example, if the upper error limit is 6.5%, you are 90% confident that the total error rate does not exceed 6.5%.

ACL uses the following formula to evaluate record errors:

Upper Error Limit Frequency = Upper Error Limit Cumulative/Sample Size

In monetary unit sampling, the upper error limit is expressed as a monetary amount and provides the “worst case” amount of error, based on the required confidence level.


In monetary unit sampling, you must use the fixed interval or cell sampling method to accurately evaluate errors. You can evaluate errors with any method of record sampling.

For monetary unit samples, the report includes the effects of each error and shows the most likely amount of total error and the upper error limit expressed as a monetary amount. This is the amount you are confident that total errors do not exceed. For example, you can estimate that the most likely errors are 50,000, but you can also be 95% confident that the total errors do not exceed 288,000.

The formula that ACL uses to evaluate monetary errors is based on the upper error limit cumulative factors for the Poisson distribution:

Related concepts
Sampling data
About sampling types
About calculating sample sizes
About sample selection methods
Monetary unit sampling options
Related tasks
Evaluating sampling errors

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