1. Dr. Donald K. McConnell Jr.
Audit Sampling (Au 350)
Dr. Donald K. McConnell Jr.
4/7/2019

2. Audit Sampling
Application of an audit procedure to < 100% of the items in an account balance or class of transactions (AU )
To form a conclusion about a population by examining only part of the data
Should not be used for balances or transactions likely to contain misstatements (AU )
Such items are broadly termed items about which we have “special knowledge” per Sampling IAG
4/7/2019

3. Circumstances in Which Sampling Does Not Apply (AU 350.32)
Tests of controls that depend primarily on appropriate segregation of duties
Tests that provide no documentary evidence of performance
Small populations: why sample?*
We can’t read every tenth line of board meeting minutes
Testing footings
*if there are three items in a population, why draw up a sampling plan? Just audit everything
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4. Nonstatistical Vs. Statistical Sampling
Both are acceptable under GAAS
Both require professional judgment in planning, performing, and evaluating a sample
Much more nonstatistical sampling done in contemporary audit practice *
Statistical sampling has one distinguishing feature:
Allows us to measure mathematically the uncertainty resulting from examining only part of the data, i.e. (AU )
Allows us to quantify sampling risk
*Even Deloitte & Touche is moving away from dollar unit sampling. They used to use stat sampling for everything virtually but writing memos, even small populations.
4/7/2019

5. More Nonstatistical Sampling Done in Contemporary Audit Practice [Hall, et al]:
June 2002 Accounting Horizons:
223 usable responses from 600 survey instruments mailed, of which:
50% were government auditors
36% were public accountants
Findings:
Only 15% using stat sampling
12% used DUS
2% used simple random sampling
74% use haphazard selection
3% used block selection
4/7/2019

6. Sampling Risk Vs. Non- Sampling Risk
Sampling risk: the risk that the sample result is not representative of the population *
Sampling risk reduced by increasing sample sizes **
Nonsampling risk: everything else that can go wrong in a test, e.g.:
Inappropriate procedures
Failure to recognize misstatements
Reduced by adequate planning and supervision
*an example: assume there are three control deviations in an entire population of 5000 transactions, and all three and up in your sample of 50 transactions!
**If you examined a population 100 percent, there can be no sampling risk.
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7. Sampling Risk: Alpha Risk Concepts (AU 350.12)
Risk of incorrect rejection [of an account balance presenting fairly]
Risk of assessing control risk too high [in a test of controls]
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8. Sampling Risk: Beta Risk Concepts
Risk of incorrect acceptance [of an account balance presenting fairly]
Risk of assessing control risk too low [in a test of controls]
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9. Why Is Alpha Risk Generally of Less Concern Than Beta Risk?
Ordinarily the auditor would expand testing, thus arriving at the correct conclusion *
The audit may be less efficient, but nevertheless effective (AU )
In practice, a bad result in testing controls usually causes the auditor to “drop back ten and punt” **
That is, expand year-end substantive testing
*what would probably happen if we tell management, based upon a test of controls, that your controls are no good!
**unless truly due to sampling error, very likely if you expand sample size, that you will still have an unacceptable result. Since we must perform year-end substantive testing in any case, just do more of it.
4/7/2019

10. Various Sampling Issues
Items for which no sampling risk is acceptable should be audited 100% (AU )
Always project likely error from known error (AU ):
Project sample error to population
Include misstatements discovered in items examined 100%
Compare this result to tolerable misstatement
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11. Always Evaluate Qualitative Aspects of Misstatements
Consider these issues:
Does the item appear to be due to error or fraud?
Does the appear to be isolated or systemic?
Fraud/systemic requires much broader consideration than error/isolated (AU )
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12. Projected Deviation Rates or Misstatements
Where projected sample misstatement or deviations less than tolerable...
Consider risk that such result might be obtained…
Even though true misstatement or deviation rate exceeds tolerable in the population (AU and .41)
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13. Nonstatistical Sampling
More widely used in practice
Oftentimes sample items selected randomly, but evaluated non statistically
Forms of nonstatistical selection:
Haphazard selection*
Block selection**
Nonstatistical sampling sizes must approximate what would be obtained from selecting a statistical sample size using reasonable parameters (Sampling Industry Audit Guide)
*selecting sample items without injecting any conscious bias: always selecting from the top of a page would be a problem
** blocks “crossing months” example
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14. Forms of Probabilistic Selection
Must be used to draw statistical inferences when using a statistical sampling approach
Random numbers software
Systematic selection:
Selecting every “n th” after a random start.
Used in PPSS (DUS) applications
Some CPA’s avoid using: what if population not randomly arranged?*
A solution: two or three random starts (e.g. three starts selecting 20 items, rather than one start selecting 60 items)
*inventory price test: expensive items always In 0. If you start with random start of 4, then select every 50th item, you would never audit the more important items. Certain transactions always at in the month
4/7/2019

15. Statistical Sampling: Attributes Plans
For testing controls
Result always projected as a percentage or rate
Forms of attributes sampling plans:
Acceptance sampling (obsolete)
Fixed sample size attributes plans (probably most commonly used in practice)
Stop or go sampling
Discovery sampling (for fraud examination)
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16. Statistical Sampling: Variables Plans
For substantive testing
Result always projects a dollar balance or error amount
Forms of variables sampling plans:
Dollar unit sampling (MUS or DUS)
Most commonly used in practice
Simplicity of attributes, but with a dollar result
Mean per unit sampling
Difference estimation: practical difficulties
Ratio estimation: many practical difficulties
4/7/2019