Audit Sampling Sampling for test of Control Substantive test sampling

Used to address sufficient audit evidence

When the application of 100% testing is not feasible or efficient

Sampling is the application of an audit procedure to less than 100 percent of the items within an account balance or class of transaction for the purpose of evaluating some characteristics of the entire balance or class

The risk the auditor’s conclusions reached from a sample maybe different from the conclusions he/she reached if the test were applied to the whole population

3 Factors affecting the determination of sample size: The desired level of assurance in the

results ( confidence level) Acceptable defect rate ( tolerable error /

materiality level ) The historical defect rate (expected error)

Confidence level is the complement of sampling risk

If the auditor set sampling risk for a particular sampling application at 5 percent, it will result to a confidence level of 95 percent

Once the desired confidence level is established, the sample size is determine largely by how much the tolerable error exceeds the expected error

Once the desired confidence level is establiched, the sample size is determine largely by how much the tolerable error exceeds the expected error

TYPES OF EVIDENCE AUDIT SAMPLING USED ?

Inspection of tangible assets

Inspection of records Reperformance Recalculation Confirmation Analytical procedure Scanning Inquiry observation

Y / N Y / N Y / N Y / N Y / N Y / N Y / N Y / N Y / N

Testing all items with a particular characteristics Population is made up with a few larger items Qualitative factors large items

E.g: choose journal entries posted by finance Director – For fraud testing over journal entries

Applicable for highly automated information systems process transactions consistently unless the systm or porgrams are changed

The auditor may test the general controls over the system and any program changes, but test only few transactons processed by the IT system

Nonstatistical ( judgemental ) Audtor does not use statistical technoques to determine

sample size, select the sample items or measure sampling risk

Statistical use the law of probability to compute sample size and evaluate the sample results

Attribute samplingMonetary unit sampling classical variable sampling

Attribute sampling Used to estimate the proportion of a population

that possess a specific characteristics. Commonly used in test for controls

Monetary unit sampling MUS uses attribute sampling theory to

estimate the dollar amount of misstatement for a class of transaction or account balance

Classical variable sampling Inference about the population based on

sample data Used when we have assessed risk as high,

we expect more than a few errors and we wish to estimate their potential monetary effects

Risk of assessing control risk too low The risk that the assessed level of control

risk based on the sample supports the planned assessed level of control risk when the true operating effectiveness of the internal control, if known, would not be considered adequate to support the planned assessed level

Risk of assessing control risk too high

Steps: Plan Perform Evaluate Document

Determine test of objectives Define the population characteristics

Define sampling population Define sampling unit Define the control deviation

Determine the sample test The desired confidence level The tolerable deviation rate The expected population deviation rate

Select sample items: Random number selection Systematic selection

Perform the audit procedures Voided document Unused or inapplicable documents Missing sample items

Calculate the sample deviation and upper deviationr ate

Draw final conclusions

Determining the sample size An auditing firm may establish a sampling

policy;i.e. low risk of failure took 15-20 samples, moderate took 25-35 samples, high took 40-60 samples

Selecting sample items Allows the use of random or systematics

selection but also permits oother method; i.e haphazard sampling ( choose sample

without bias )

Calculating the upper deviation rate Auditor can calculate the sample deviation

rate, but cannot quantify the computer upper deviation rate and sampling risk associated with the test

Risk of incorrect acceptance ( type 1) The risk that sample supports the

conclusion that the recorded account balance is not materially misstated when it is materially misstated

Risk of incorrect rejection ( type 2 )

MUS uses attribute sampling theory to estimate the percentage of monetary units in a population that might be misstated and then multiplies this percentage by an estimate of how much the dollars are misstated. Commonly used for test of details

When the auditor expects no misttatemens, MUS usually result in a smaller sample size than classical variable sampling

The calculation of the sample size and evaluation of the sample results are not b ased on the variation between items in t epopulation

Whenapplied using the probability to proportional to size procedure, MUS automatically results in a stratified sample

The selection of zero or negative balances generally requires special design consideraton

The general approach to MUS assumes that the audited amount of the sample item is not in the error by more than 100%

When more than one or two misstatements are detected, the sample results calculations may overstate the allowance for sampling risk

MUS is not effective in detecting understatements

The sampling unit for non statistical sampling is normally a customer account, an individual transaction or a line item on a transactions

When using non statistical sampling, the following items must be considered Identify individually significant items Determining the sample size Selecting sample items Calculation the sample result

Uses normal distribution theory to evaluate the characteristics of a population based on sample data. Auditors commonly use classical variables sampling to estimate the size of misstatement

When the auditors expect a large number of differences between book and audited values this method will result in smaller sample size than MUS

The techniques are effective for both overstatements and understatements

The selection of zero balance generally does not require special sample design considerations

To determine sample size, the auditor must estimate the standard deviation of audited value of differences

If few misstatements are detected in the sample data, the true variance tends to be underestimated, and the resulting projection of the misstatements to the population is likely not to be reliable