Module E

1. Module E Overview of Sampling Mudule E

2. 1. Introduction • Definition • Primary advantage – efficiency • Primary disadvantage – effectiveness • Necessary knowledge • Uses of sampling in auditing Mudule E

3. 2. Types of Sampling • Nonstatistical • use judgment to select sample and/or evaluate results • justification for use • Statistical • use random selection • evaluate results mathematically (mathematically measure sampling risk) • advantages Mudule E

4. 3. Statistical Sampling Models • Attribute Sampling • fixed sample-size attribute sampling • discovery sampling • Variables Sampling • classical methods • MPU, ratio estimation, difference estimation • PPS Mudule E

5. Random SelectionTechniques • Random number table • Computer generation • Systematic selection • Block sampling • Stratification Mudule E

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 Mudule E

7. 6. Sampling Risks • Terms • errors – deviations – misstatements • expected deviation rate • tolerable deviation rate • precision • reliability Mudule E

8. 6. Sampling Risks (continued) • Sampling risk • 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 Mudule E

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10. Attribute Sampling (Module F) Mudule E

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) Mudule E

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 Mudule E

13. 7. Attribute Sampling (Con’t) • Sample Size Table (5% risk) Tolerable Deviation Rate EDR 2% 3% 4% 5% 6% 7% 1.00% * * 156 93 78 66 2.00% * * * 181 127 3.00% * * * * 195 129 88 Mudule E

14. 7. Attribute Sampling (Con’t) • Evaluation Table (5% risk) No. of deviations found n 0 1 2 3 4 5 75 4.0 6.2 8.2 10.1 11.8 13.6 80 3.7 5.8 7.7 9.5 11.1 12.7 90 3.3 5.2 6.9 8.4 9.9 11.4 7.7 Mudule E

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) Mudule E

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) Mudule E

17. 9. Examples - Attribute Sampling • Case ABCDRisk of Overreliance 5% 5% 10% 10%Expected Deviation Rate 2% 4% 2% 4%Tolerable Deviation Rate 7% 9% 7% 9% Errors Found 1 3 0 2 Results Mudule E

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 Mudule E