1 / 35

Sampling for Tests of Transactions

Sampling for Tests of Transactions. Objective of Tests TOC - Determine if control is effective STOT - Determine existence of monetary errors in transactions. Objective of TOC and STOT. Both are tests of transactions Evaluated yes / no control is / is not effective

lotus
Download Presentation

Sampling for Tests of Transactions

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Sampling for Tests of Transactions • Objective of Tests • TOC - Determine if control is effective • STOT- Determine existence of monetary errors in transactions

  2. Objective of TOC and STOT • Both are tests of transactions • Evaluated yes / no • control is / is noteffective • transaction is / is notcorrectly recorded • Auditor should consider qualitative aspectsof deviations

  3. Evaluation of Results • TOC- assessment of control risk • affects PDR and evidence (STOT and TODB) • STOT - are transactions correctly recorded • affects extend to TODB • also has implications for control risk

  4. Risk Model • TOC CR • Lower CR PDR • Evidence • (less STOT & TODB for related objective)

  5. Risk Model • STOT • CR PDR • STOT affect CR assessment. • Good results also lowers TODB • (STOT and TODB both part of PDR)

  6. Relationship between selection and evaluation • Statistical Sampling must be • evaluation random(probabilistic) • Nonstatistical Judgmental selection • evaluation allowed, but random • selection is still preferable • Point: Random selection is always desirable

  7. Selection Methods • Random - Each item has equal chance of selection (TOT) • Probabilistic - each item has a know probability of selection (TODB)

  8. Sampling Risk • Sampling Risk- Risk the sample is not representative; inherent with sampling. Reduce by: • Using appropriate sampling • Increasing sample size • Is a random sample representative?

  9. Nonsampling Risk • Nonsampling error- is the risk the auditor fails to uncover existing errors in the sample. Reduce by: • Carefully defining and performing the test(text example of incorrectly selecting an existence sample from shipping documents) • Control over sample

  10. 15-27 (a) - Test with attributes sampling? • Review CR journal for unusual transactions. • Trace prelisting amts. to CR journal • Compare info on prelisting with CR journal • Examine remittance advices to see if discounts approved • Trace entries from prelist to deposit

  11. 15-27 (a) - Test with attributes sampling? • Review CR journal for unusual transactions. • Trace prelisting amts. to CR journal • Compare info on prelisting with CR journal • Examine remittance advices to see if discounts approved • Trace entries from prelist to deposit

  12. Sample Selection • 2. Trace prelisting amts. to CR journal (completeness) • 3. Compare info on prelisting with CR journal (accuracy) • 4. Examine remittance advices to see if discounts approved (accuracy) • 5. Trace entries from prelist to deposit (completeness) • Draw sample from prelist (source for directional completeness tests)

  13. Sampling for Exception Rates • Objective- Estimate maximum error rate in population based on sample • determine if control can be relied on or results for STOT acceptable

  14. Maximum Error Rate (CUER) • Sample exception rate (SER) is best estimator of population exception rate • Maximum rate is the computed upper exception rate (CUER) and includes allowance for sampling risk (ASR) • CUER = SER + ASR

  15. Larger n • sample exception rateCUER • (SER)

  16. Variable Description Definition • EPER Expected Last year’s results • population or auditor exception rate expectations • TER Tolerable Maximum rate • (precision) exception that auditor will rate allow. Lower for imp. objectives • ARACR Acceptable Risk of relying • (confidence risk of on control/STOT level) assessing CR when it is too low ineffective

  17. Changes in variables on sample size • Planning relation: • TER = EPER + ASR (allowance for sample risk) • Increases in sample size lower sampling risk

  18. Effect on Changes in Variables on • Sample Size • Change in Effect • Factor on n Reason • EPER increase Increase Less allowance for • sampling risk for given TER • TERincrease Decrease More allowance for sampling risk for given EPER • ARACR Decrease Less reliance on increase controls; less evidence needed

  19. The biggest effect on sample size is TER - EPER • Bigger differences provides larger allowance for sampling risk

  20. 15-22(a) • If all other factors remain constant, changing ARACR from 10% to 5% would cause sample size to: • 1. Increase • 2. Remain the same • 3. Decrease • 4. Become indeterminate

  21. Problem 15-28 Part c: (increase in ARACR) • 12 34 • ARACR 10 5 5 5 • TER 6 6 5 6 • EPER 2 2 2 2 • Population • size 1,000 100,000 6,000 1,000 • Sample • Size 88 127 181 127

  22. Problem 15-28 Part c: (increase in TER) • 1 2 34 • ARACR 10 5 5 5 • TER 6 6 5 6 • EPER 2 2 2 2 • Population • size 1,000 100,000 6,000 1,000 • Sample • Size 88 127 181127

  23. Problem 15-28 Part c: (increase in EPER) • 5 67 • ARACR 10 10 5 • TER 20 20 2 • EPER 8 2 0 • Population • size 500 500 1,000,000 • Sample • Size 25 18149

  24. Problem 15-28 Part c: (increase in POP) • 1 2 3 4 • ARACR 10 5 5 5 • TER 6 6 5 6 • EPER 2 2 2 2 • Population • size 1,000 100,000 6,000 1,000 • Sample • Size 88 127181127

  25. Evaluating the Sample • TER, ARACR, and n as before • SER = Sample exception rate • (# of exceptions/n) • CUER = maximum error rate • = SER + ASR

  26. Evaluation Decision - Nonstatistical sampling • Approach 1: • Accept if CUER < TER • Approach 2: • ASR = TER - SER • Accept if ASR large enough (SER sufficiently less than TER)

  27. Options when CUER exceeds TER • 1. Increase sample size - lowers ASR and CUER • (may also reduce SER if initial sample not representative) • 2. Don’t rely upon control/STOT • CR and substantive tests

  28. Options when CUER > TER • TOC fails -can increase STOT or TODB. Usually increase STOT • STOT fails- Increase TODB • 3. Revise TER or ARACR - • usually not recommended

  29. Change in variables on CUER • Evaluation relation: • CUER = SER + ASR • Remember: increase in sample size lowers sampling risk • Increase in n may also lower SER if initial sample not representative

  30. Effect of Changes in Variables on CUER • Change in Effect on • Factor CUER Reason • SER Increase Increase CUER = SER+ASR • n increase Decrease Lower sampling risk • ARACR Decrease Less reliance on • increase controls; threshold for accepting a control is lower

  31. Lower ARACR is farther out curve • Larger n • sample exception rateCUER • (SER) 5% ARACR

  32. 15-23 (a) • An auditor estimates with 10% ARACR (90% confidence) that CUER is between 4% and 6%. The auditor’s major concern is that there is one chance in 20 that the true exception rate is: • 1. More than 6% • 2. Less than 6% • 3. More than 4% • 4. Less than 4%

  33. Problem 15-29 Part c: (decrease ARACR) • 1 23 4 • ARACR 10 5 5 5 • Population Size 5,000 5,000 5,000 50,000 • Sample size 200 200 50 200 • Number of • exceptions 4 4 1 4 • CUER 4.0 4.59.1 4.5

  34. Problem 15-29 Part c:(decrease n) • 1 2 3 4 • ARACR 10 5 5 5 • Population Size 5,000 5,000 5,000 50,000 • Sample size 200 200 50 200 • Number of • exceptions 4 4 1 4 • CUER 4.0 4.5 9.1 4.5

  35. Problem 15-29 Part c:(decrease exceptions) • 5 6 7 8 • ARACR 5 5 5 5 • Population Size 500 900 5,000 500 • Sample size 100 100 100 25 • Number of • exceptions 2 10 0 0 • CUER 6.2 16.4 3.0 11.3

More Related