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

Audit Sampling Application of an audit procedure to < 100% of the items in an account balance or class of transactions (AU 350.01) 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 350.02) Such items are broadly termed items about which we have “special knowledge” per Sampling IAG 4/7/2019

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 4/7/2019

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 350.46) 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

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

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. 4/7/2019

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] 4/7/2019

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] 4/7/2019

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 350.13) 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

Various Sampling Issues Items for which no sampling risk is acceptable should be audited 100% (AU 350.21) Always project likely error from known error (AU 350.26): Project sample error to population Include misstatements discovered in items examined 100% Compare this result to tolerable misstatement 4/7/2019

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 350.27) 4/7/2019

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 350.26 and .41) 4/7/2019

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 4/7/2019

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

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) 4/7/2019

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