Download - Audit Sampling Pertemuan 15-16 Matakuliah: A0294/Audit SI Lanjutan Tahun: 2009

Audit Sampling Pertemuan 15-16

Matakuliah : A0294/Audit SI Lanjutan Tahun : 2009

Bina Nusantara University 2

Learning Outcomes

Pada akhir pertemuan ini, diharapkan mahasiswa akan mampu :• Memahami teknik penentuan ukuran sample• Memahami teknik pengambilan sample


Attribute sampling

• It is a statistical method used to estimate the proportion of a characteristic in a population (absence or presence of a control)

• The auditor is normally attempting to determine the operating effectiveness of a control procedure in terms of deviations from the prescribed control


Attribute sampling

• Planning– 1st - Determine the objective(s) of the test– 2nd - Define the control deviation conditions.– 3rd - Define the population– 4rd - Define the period covered by the test– 5th - Define the sampling unit.– 6th –Define the technical parameters to determine the

sample size


Attribute sampling

• Sample size

– It could be determined using:• Mathematic formulas• Statistic tables• software (CAAT: ACL or IDEA)


Attribute sampling

• Select the sample, perform the tests and evaluate the results– Selection using:

• Tables of random numbers• software (CAATS: ACL or IDEA or EXCEL)

– Evaluation of results using:• Satistic tables • Software (CAATS: ACL or IDEA or EXCEL)


Attribute sampling

• Key concepts– Confidence level, say 90%(complement of risk of

incorrect acceptance: 10%)– Expected deviation rate (EDR)– Maximum Tolerable deviation rate (MTDR)– Precision of the test (P<=MTDR-ER)– Confidence interval– Sample size– Results evaluation


Attribute sampling

Confidence level (CL) or reliability level (RL) It is a probability of the auditor being correct in

his/her evaluation of the risk of control (genneraly varies from 90 to 99%)

CL is 100% if all the items in the population are examined


Attribute sampling

• Risk (2 types of sampling risks):– Risk of assessing control risk too low (complement of reliability

or confidence level)- is the probability that the sample supports the conclusion that the controls are operating effectively when they are not (we fail to recognise unreliable controls and setting CL for operations at a lower level)

– Risk of assessing control risk too high- is the probability that the sample supports the conclusion that the controls are not operating effectively when this is not the case (this situation causes setting of CL for audit on operations at a higher level and more unnecessary audit work)


Attribute sampling

Expected deviation rate (EDR)

• Maximum expected of deviations in the population based on last year audit or a pilot test

• EDR allows to define an interval of confidence for the sample identifying its precision (precision<=MTDR-EDR)

• The bigger EDR (more per cent or number of errors) the larger is the sample size


Attribute sampling

0,00% 149 (0) 99 (0) 74 (0) 59 (0) 49 (0) 42 (0) 36 (0) 32 (0) 29 (0)

0,25% 236 (1) 157 (1) 117 (1) 93 (1) 78 (1) 66 (1) 58 (1) 51 (1) 46 (1)

0,50% 157 (1) 117 (1) 93 (1) 78 (1) 66 (1) 58 (1) 51 (1) 46 (1)

0,75% 208 (2) 117 (1) 93 (1) 78 (1) 66 (1) 58 (1) 51 (1) 46 (1)

1,00% 156 (2) 93 (1) 78 (1) 66 (1) 58 (1) 51 (1) 46 (1)

1,25% 156 (2) 124 (2) 78 (1) 66 (1) 58 (1) 51 (1) 46 (1)

1,50% 192 (3) 124 (2) 103 (2) 66 (1) 58 (1) 51 (1) 46 (1)

1,75% 227 (4) 153 (3) 103 (2) 88 (2) 77 (2) 51 (1) 46 (1)

2,00% 181 (4) 127 (3) 88 (2) 77 (2) 68 (1) 46 (1)

2,25% 208 (5) 127 (3) 88 (2) 77 (2) 68 (1) 61 (2)

2,50% 150 (4) 109 (3) 77 (2) 68 (2) 61 (2)

2,75% 173 (5) 109 (3) 95 (3) 68 (2) 61 (2)

3,00% 195 (6) 129 (4) 95 (3) 84 (3) 61 (2)

3,25% 148 (5) 112 (4) 84 (3) 61 (2)

3,50% 167 (6) 112 (4) 84 (3) 76 (3)

3,75% 185 (7) 129 (5) 100 (4) 76 (3)

4,00% 146 (6) 100 (4) 89 (4)

5,00% 158 (8) 116 (6)

Confidence level: 95%MTDREDR

2,00% 3,00% 4,00% 5,00% 6,00% 7,00% 8,00% 9,00% 10,00%


Attribute sampling

0 1 2 3 4 525 11,3 17,630 9,5 14,9 19,635 8,3 12,9 17,040 7,3 11,4 15,0 18,345 6,5 10,5 13,4 16,4 19,250 5,9 9,2 12,1 14,8 17,4 19,955 5,4 8,4 11,1 13,5 15,9 18,260 4,9 7,7 10,2 12,5 14,7 16,865 4,6 7,1 9,4 11,5 13,6 15,570 4,2 6,6 8,8 10,8 12,6 14,575 4,0 6,2 8,2 10,1 11,8 13,680 3,7 5,8 7,7 9,5 11,1 12,790 3,3 5,2 6,9 8,4 9,9 11,4

100 3,0 4,7 6,2 7,6 9,0 10,3125 2,4 3,8 5,0 6,1 7,2 8,3150 2,0 3,2 4,2 5,1 6,0 6,9200 1,5 2,4 3,2 3,9 4,6 5,2

Fonte: Guy, D.M., Audit Sampling: na introduction , 58

Sample size

Number of errors foundUpper error limit rate confidence level: 95%


Variables sampling

Auditor problem:• Is there the possibility of overstatement in value

of some item of the population which could result in a qualified opinion?


Variables sampling statistical methods

• Sampling proporcionate to size (PPS/MUS)• Classical variables sampling:

– Direct projection (stratification required);– Ratio or difference estimation (when errors are fairly

frequent)


Monetary Unit Sampling (MUS)

The classical variables sampling can fail in the detection of the material mistake because:– The methods of selection are random and based on

physical registers (we maynot choose a document that contains a mistake)

– There is a possibility not to project this mistake for the population, so there is the possibility not to detect one material mistake



• Auditor precautions:– examine all the big transactions that individually can

contain a material mistake– apply a method of sampling for the remaining items on

basis of a stratified selection (nevertheless the stratification does not remove totally the problem of the possibility not to select a group of items that totalizes a material mistake)



• Another approach:– as bigger values are the most relevant items for the

auditor the suggested new approach makes a selection based on the weight of monetary value, generically designated by probability-proportional-to-size (PPS) or monetary unit sampling (MUS)



• Evaluation of results

– The Poisson distribution is used for the evaluation of results:

– Tables– Audit Software (ACL, IDEA, Etc…)– Specific programmes



• Opinion formulation

– The auditor, based on the evidence of the sample, declares, with a CL of y %, an upper error limit in the population (where the amount depends on the results of the sample, for a sample size of n and for a number x errors found in the sample).



– Risk– Monetary precision (MP)– Expected error– Tolerable error (materiality threshold)– Sample size– Confidence factor R (Poisson) – Expansion factors for expected error– Upper error limit



• Risk (2 types of sampling risks):– Risk of incorrect acceptance- is the probability

that the sample supports the conclusion that the book value is not materially misstated when it is (not identified qualified opinion)

– Risk of incorrect rejection- is the probability that the sample supports the conclusion that the book value is materially misstated when it is not (this situation cause more audit work)



Monetary precision (MP)

It is a basic threshold to determine the sample size that means, the auditor expects a maximum amount of overstatement in the population equivalent to the MP even if there are no misstatements in the sample, for a certain confidence level (auditor judgement)



Expected error (EE)– Based on last year experience and inherent risk

evaluation, considering the results of tests of controls and other procedures, expected error is the amount of overstatement the auditor expects to find in the population

– It is used to control the risk of incorrect rejection due to a small sample size (increases the initial sample size)



Tolerable error (TE)– It is the maximum overstatement error for a population of

accounts or group of transactions that can be accepted by the auditor without causing material error in the financial demonstrations (judgement of the auditor)

– It is compared against the results of the sample in order to decide whether the auditor accepts the population as free of material errors or alternatively qualifies the opinion

– Commission stated in Annex IV CR 1828 that the maximum materiality threshold is 2%


Nature and Cause of Errors

• The auditor should consider:– The nature – The cause

In order to evaluate the sample results this means that he have to classify the errors before the extrapolation:

• anomalous error• Systematic error

• Random error


Errors Evaluation

Population

Sample

Systemic errorKnown

Random error

unknown Random error


Errors Evaluation

• anomalous error

– The error arises from an isolated event that has not occurred other than on specifically identifiable occasions and is therefore not representative of similar errors in the population

the auditor has to have a high degree of certainty that such error is not representative of the population


Errors Evaluation

• Systematic error– is a non-random exception that is likely to have

occurred more than once on positively identifiable occasions.

– The auditor obtains the effect of each systematic error on the total population by performing additional audit in order to make a qualitative evaluation of how, why, when and where the systematic error occurred


Using Microsoft Excel for Sampling


The End

Download - Audit Sampling Pertemuan 15-16 Matakuliah: A0294/Audit SI Lanjutan Tahun: 2009

Top Related