1. Module E
Overview of Sampling
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2. 1. Introduction Definition Primary advantage – efficiency
Primary disadvantage – effectiveness
Necessary knowledge
Uses of sampling in auditing
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3. 2. Types of Sampling Nonstatistical Statistical
use judgment to select sample and/or evaluate results
justification for use
Statistical
use random selection
evaluate results mathematically (mathematically measure sampling risk)
advantages
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4. 3. Statistical Sampling Models
Attribute Sampling
fixed sample-size attribute sampling
discovery sampling
Variables Sampling
classical methods
MPU, ratio estimation, difference estimation
PPS
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5. Random Selection Techniques
Random number table
Computer generation
Systematic selection
Block sampling
Stratification
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6. 5. Sampling Plan
Steps
Specify audit objectives and select sampling method
Define errors
Define population
Determine sample size
Select sample
Apply audit procedures
Evaluate results
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7. 6. Sampling Risks Terms errors – deviations – misstatements
expected deviation rate
tolerable deviation rate
precision
reliability
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8. 6. Sampling Risks (continued)
the risk you reach the wrong conclusion as a result of the sample
Nonsampling risk
the risk you reach the wrong conclusion as a result of anything other than the sample
Sampling Risk
Risk of underreliance (Risk of assessing CR too high) Risk of incorrect rejection
Risk of overreliance (Risk of assessing CR too low) Risk of incorrect acceptance
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9. True Population State is: Sample Results Tell You Population is:
Sampling Risk
True Population State is:
Correct
Not Correct
Sample Results Tell You Population is:
Correct Conclusion
β risk
α risk
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10. Attribute Sampling (Module F)
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11. 7. Attribute Sampling
Used to estimate the extent to which a characteristic (error rate) exists within a population
Used in tests of controls
Examine sample to estimate the rate at which an internal control is not functioning as intended (error rate)
Then compare that rate to the allowable rate (tolerable error rate)
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12. 7. Attribute Sampling Example Risk of assessing CR too low: 5% (ROO)
Tolerable deviation rate: 7% (TRD)
Expected deviation rate: 2% (EPDR)
Actual number of deviations found: 2
Use tables on pages: 832, 833, 834
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13. 7. Attribute Sampling (Con’t)
Sample Size Table (5% risk)
Tolerable Deviation Rate
EDR 2% 3% 4% 5% 6% 7%
1.00% * *
2.00% * * *
3.00% * * * * 88
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14. 7. Attribute Sampling (Con’t)
Evaluation Table (5% risk)
No. of deviations found n
7.7
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15. 8. Evaluate Sample Results
If UL (ULRD) > Tolerable Deviation Rate:
Conclude that internal control is not functioning effectively
Options
Increase sample size in hopes of supporting planned level of control risk (rarely done)
Increase level of control risk, leading to conducting more, and more effective, substantive procedures (lower detection risk)
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16. 8. Evaluate Sample Results (Con’t)
If UL (ULRD)  Tolerable Deviation Rate
Conclude that the internal control is functioning effectively
Options
Maintain planned level of control risk, leading to conducting the planned amount of substantive tests
Consider a further reduction in control risk, leading to conducting fewer substantive procedures (higher detection risk)
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17. 9. Examples - Attribute Sampling
Case A B C D Risk of Overreliance % % % % Expected Deviation Rate % % % % Tolerable Deviation Rate % % % %
Errors Found
Results
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18. 10. Review Questions for Discussion
Module E
E.1
E.2
E.3
E.4
E.5
E.6
E.10
E.11
E.14
E.15
E.16
E.17
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