Audit Final Exam

When designing audit procedures - auditors can select items for testing by: 

• Selecting all items (100% examination) 

• Selecting specific items 

• Audit sampling 

All items appropriate when

• Not efficient often 

• Certain cases it is okay

  1. Population is made up small number of large value items that individually exceed performance materiality 

  2. When performing tests of details if the risk of error is expected to be high then 100% or use or large sample size

  3. If auditor plans to use audit data analytics it may be easier to efficiently test entire population for certain attributes or other process 

Selecting specific items

• High value or key items could individually result in a material misstatement 

• All items over a specified value 

• Any unusual or sensitive items or FS disclosure 

• Any items highly susceptible to misstatement 

• Items to test control activities (auditor may use judgement to select and examine specific items to determine if a particular control activity is being performed) 

Sampling

• Representative sample of items from the population 

  1. For reaching a conclusion about an entire population (data set) by selecting and examining a representative sample 

• Stratification of the population 

  1. Separates the population into distinct groups of items with similar characteristics 

  2. Eg. all large items can be extracted and tested separately and then a representative sample could be taken from the remaining items 

Audit sampling

• The application of audit procedures to less than 100% of the items in the population 

• While ensuring all sampling units have a chance of selection 

• In order to provide a reasonable basis to make a conclusion on the entire population 

Sampling

• key part of auditor’s risk response 

Population

items that make up a class of transactions or an account balance 

Representative sampling

sample in which the characteristics in the sample are approx. the same as the population as a whole 

Risk of incorrect conclusions are due to

• Sampling error → called sampling risk 

  • Risk an auditor reaches an inappropriate conclusion because the sample is not representative of the population 

• Non sampling error → called nonsampling risk

  • Risk an auditor makes an incorrect conclusion due to failure to recognize exceptions and/or inappropriate or ineffective audit procedures 

Two ways to control sampling risk: 

1. Adjust sample size 

   • Increasing sample size reduces risk (sample of all items of a pop. has zero risk - this is not acc. sampling) 

2. Use an appropriate method of selecting sample items in the population  

• Audit sampling methods can be divided into two categories: 

• Statistical sampling → applies mathematical rules so that auditors can quantify (measure) sampling risk in planning the sample (step 1) and evaluating the results 

• Nonstatistical sampling → auditors use their professional judgement in considering the effect of sampling risk 

• Both categories involve three phases 

• Step 1: Planning - plan the sample and determine sample size

• Step 2: Performance - select the sample and perform the tests 

• Step 3: Evaluation - evaluate the results and conclude on the acceptability of the population tested 

Probabilistic VS Nonprobabilistic Sample Selection 

Fall under performance (step 2) 

• Probabilistic sample selection: the auditor randomly selects items such that each population item has a known probability of being included in the sample 

• Nonprobabilistic sample selection: the auditor selects sample items using non probabilistic methods that approximate a random sampling approach 

Common sample selection methods

Simple random sample selection:

Simple random sample selection: every possible combo of population items has an equal chance of being selected 

• Often software is used for random numbers

Systematic sample selection:

Systematic sample selection: auditor calcs an interval and selects the items for the sample based on the size of the interval and a randomly selected number between zero and the sample size 

• Easy to use  

• Possibility of bias 

Probability proportionate to size sampling or Monetary unit sampling (MUS) or dollar sampling:

modified form of systematic sample selection. Focuses on the individual dollar (or unit of currency. Eg. euro) as the unit of interest.

Directed sample selection:

each item in the sample is selected on the basis of some judgmental criteria

Block sample selection:

auditors select the first item in a block and the remainder of the block is chosen in sequence

Haphazard sample selection:

 items chosen without regard to size, source or other distinguishing characteristics 

Stratified sampling:

 all the elements in the total population are divided into two or more subpopulations that are tested independently 

Audit Sampling Process for Tests of Controls and Substantive Tests of Details

Using Sampling for Tests of Controls 

• Help conclude if the control is operating as intended 

• Sampling for exceptions: auditors estimate the percent of items a population containing a characteristic or attribute of interest 

• Exception: refer to deviation from the client’s control procedure and amounts not monetarily correct 

• Also use statistical sampling for tests of controls. If used to reach conclusion about exception rate this is called attributes sampling 

Nonstatistical sampling for substantive tests 

• tolerable misstatement

• acceptable risk of incorrect acceptance

• Tolerable misstatement: application of performance materiality to a particular sampling procedure 

• Planned sample size increases as the amount of misstatements expected in the population approaches tolerable misstatement 

• Acceptable risk of incorrect acceptance (ARIA): risk that the auditor is willing to take of accepting a balance as correct when the true misstatement in the balance is greater than tolerable misstatement 

Nonstatistical sampling for substantive tests of details: projecting misstatements 

Nonstatistical sampling for substantive tests - eval the results

• Eval the sample results: quantitative eval, request client to correct known misstatements, qual assessment 

• Conclude on acceptability of population 

• Take no action until tests of other audit areas are complete

• Perform expanded audit tests in specific areas

• Increase sample size 

• Adjust the account balance 

• Refuse to give an unqualified opinion 

Steps of Audit Sampling Process

Audit data analytics VS sampling 

• If using data analytical tools may be more efficient to test entire population for certain attributes/process 

• ADAs can be used for tests of controls, substantive test, and substantive analytical procedures

Auditor may use ADAs when 

• There is data that is relevant for the assertion or attribute that is being testing (ADA is most suited for testing accuracy and cut-off assertions)

• The data is reliable (if it is from the client, then there are strong controls in place or if it is from an external source, that source is reliable)

• The data is accessible and has been scrubbed of errors so it is in a usable format