Audit Sampling Pertemuan 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

Bina Nusantara University 3 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

Bina Nusantara University 4 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

Bina Nusantara University 5 Attribute sampling Sample size –It could be determined using: Mathematic formulas Statistic tables software (CAAT: ACL or IDEA)

Bina Nusantara University 6 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)

Bina Nusantara University 7 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

Bina Nusantara University 8 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

Bina Nusantara University 9 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)

Bina Nusantara University 10 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

Bina Nusantara University 11 Attribute sampling

Bina Nusantara University 12 Attribute sampling

Bina Nusantara University 13 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?

Bina Nusantara University 14 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)

Bina Nusantara University 15 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

Bina Nusantara University 16 Monetary Unit Sampling (MUS) 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)

Bina Nusantara University 17 Monetary Unit Sampling (MUS) 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)

Bina Nusantara University 18 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

Bina Nusantara University 19 Monetary Unit Sampling (MUS) 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).

Bina Nusantara University 20 Monetary Unit Sampling (MUS) –Risk –Monetary precision (MP) –Expected error –Tolerable error (materiality threshold) –Sample size –Confidence factor R (Poisson) –Expansion factors for expected error –Upper error limit

Bina Nusantara University 21 Monetary Unit Sampling (MUS) 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)

Bina Nusantara University 22 Monetary Unit Sampling (MUS) 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)

Bina Nusantara University 23 Monetary Unit Sampling (MUS) 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)

Bina Nusantara University 24 Monetary Unit Sampling (MUS) 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%

Bina Nusantara University 25 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

Bina Nusantara University 26 Errors Evaluation

Bina Nusantara University 27 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

Bina Nusantara University 28 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

Bina Nusantara University 29 Using Microsoft Excel for Sampling

Bina Nusantara University 30 The End