Estimated Attribute Error Rate
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procedure on less than one hundred percent of the population. It represents the risk that the audit sample is not representative expected error rate of the population. In other words, that the auditor's evaluation of
Expected Error Rate Naive Bayes
a population based on an audit sample is different from what it would be if the entire population expected error rate definition was tested. Nonsampling risk results from human error. It represents the risk that the selected audit procedure is not appropriate for the intended purpose or the evidence from
Expected Population Deviation Rate
an audit procedure is misinterpreted. Nonsampling risk exists regardless of the number of items selected from a population for testing. Sampling risk should be considered when an auditor performs an audit procedure on less than one hundred percent of a clearly definable population for the purpose of evaluating the population. There are two aspects to sampling risk attribute sampling tables when performing tests of controls: The risk of assessing control risk too low represents the risk that an audit sample supports the conclusion that the design and operation of an internal control is effective when in fact it is not. The risk of assessing control risk too high represents the risk that an audit sample supports the conclusion that the design and operation of an internal control is not effective when in fact it is effective. Similarly there are two aspects to sampling risk when performing substantive tests: The risk of incorrect acceptance represents the risk that an audit sample supports the conclusion that a material misstatement does not exist when in fact a material misstatement does exist. This risk is similar to the risk of assessing control risk too low. The risk of incorrect rejection represents the risk that an audit sample supports the conclusion that a material misstatement exists when in fact a material misstatement does not exist. This risk is similar to the risk of assessing contr
looks like your browser does not have JavaScript enabled. Please turn on JavaScript and try again. Attribute Sampling PlansA simple statistical application may dramatically improve the reliability of internal control testing. Dennis Applegate January 01, 2010 Comments
Attribute Sampling Calculator
Views Page Image Page ContentA reliability assessment of the organization's internal
Attribute Sampling Vs Variable Sampling
control system involves deciding how much evidence to gather. Because an examination of all underlying control data is attribute sampling vs variable sampling audit not always feasible, auditors must often draw samples, audit the items selected, and extrapolate the results to the larger population.Either a statistical or nonstatistical approach to sampling is http://www4.ncsu.edu/unity/users/b/buckless/www/AUSection350.html acceptable under The IIA'sInternational Standards for the Professional Practice of Internal Auditingand The American Institute of Certified Public Accountants' (AICPA's) Professional Auditing Standards. The use of statistics, however, will help auditors develop sample plans more efficiently and assess sample results more objectively than nonstatistical methods alone. Even a well-designed nonstatistical sample cannot measure the risk that the sample https://iaonline.theiia.org/attribute-sampling-plans is not representative of the population - a distinct advantage of statistically based sampling plans. Moreover, increased regulatory requirements to provide greater assurance over internal accounting controls and company demands for greater productivity from their audit shops make statistical sampling a necessary part of the internal auditor's tool kit. Fortunately, auditors can use statistical sampling techniques without any detailed knowledge of classical statistical theory and still accomplish their audit objectives.AttributeSamplingAttribute sampling plans represent the most common statistical application used by internal auditors to test the effectiveness of controls and determine the rate of compliance with established criteria. The results of these plans provide a statistical basis for the auditor to conclude whether the controls are functioning as intended, reflecting either control compliance or noncompliance - a binary (yes/no) proposition.In developing an attribute sampling plan, the auditor must first define the audit test objective, population involved, sampling unit, and control items to be tested. For example, if the auditor's objective is to determine the percentage of sales orders lacking credit approval, the p
Backup Risk Management Solutions AuditSampler Contact Us ← Statistical Sampling Monetary Unit Sampling → Attributes Sampling Posted on April 15, 2014 by cplusglobal Attributes sampling is a statistical sampling method used to https://cplusglobal.wordpress.com/2014/04/15/attributes-sampling/ determine if items sampled from a population contain certain attributes or qualities. For example, all purchases are required to be authorized by the manager initialing on the purchase order form. The manager's authorization is the attribute and the sampling unit is the purchase order form. Attributes sampling is typically used for tests of controls to determine if the prescribed internal control procedures are being implemented. The method error rate is also used in quality control and batch/lot testing to determine product quality or defects. Determine Attributes Sample Size The sample size can be determined by using Attributes sampling tables (e.g. AICPA audit guide sampling tables) or statistical audit sampling software. The inputs required are: Expected Error Rate (EER): the expected rate of error in the population. Tolerable Error Rate (TER): the tolerable margin of error or precision for expected error rate the sample estimate of the population (i.e. precision limit). Confidence Level (CL): the level of assurance required (i.e. complement of risk of over-reliance). Assuming: EER=1%, TER=7% and CL=90% From the Attributes Sample Size table below: ⇒ Match the Expected Error Rate (1.00) to the Tolerable Error Rate (7%); ⇒ Sample size and the expected number of errors = 55 (1). If statistical audit sampling software is used (e.g. AuditSampler), the required sample size would be computed numerically based on the cumulative Binomial Distribution formula. Select Sample Items Sample items are selected from the population based on either random selection (using a random number generator or table) or systematic selection to pick every nth item after an initial random start. For example based on systematic selection, if the population consists of 1,000 purchase orders (P102001 - P103000) then the sampling interval would be 18 (i.e. 1000/55). The random start would be a number between 1 and the sampling interval. The item from the population that equals the random start (i.e. 102006) would be selected as the first sample. The remaining samples are selected from subsequent items in the population that coincide with the sampling interval (i.e. 102024, 102042, 102060, 102078, etc). Evaluate Sample Results Sample results are evaluat