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Денежная выборочная проверка (Monetary Unit Sampling)×Атрибутное выборочное тестирование в аудите×
ОбластьБухгалтерский учётБухгалтерский учёт
СемействоMCDMMCDM
Год появления19721972
Автор методаAmerican Institute of Certified Public Accountants (AICPA) and audit theoristsAmerican Institute of Certified Public Accountants (AICPA)
ТипStatistical sampling technique for substantive testingStatistical sampling technique for control testing
Основополагающий источникAmerican Institute of Certified Public Accountants (AICPA). (2015). Audit Sampling. AU-C Section 530. AICPA Professional Standards. link ↗American Institute of Certified Public Accountants (AICPA). (2015). Audit Sampling. AU-C Section 530. AICPA Professional Standards. link ↗
Другие названияDollar Unit Sampling, MUS, Cumulative Monetary Amount SamplingStatistical Attribute Sampling, Compliance Testing, Control Testing Sampling
Связанные33
СводкаMonetary Unit Sampling (MUS) is a statistical sampling method widely used in audit substantive testing that selects individual dollar amounts from an account population rather than individual transactions. This approach is particularly effective for testing the correctness of financial statement balances because large-dollar items are automatically included more frequently in the sample, making it efficient for detecting material misstatements.Attribute sampling is a statistical sampling method used primarily in testing the operating effectiveness of internal controls. Rather than measuring the dollar impact of errors (as in substantive sampling), attribute sampling answers a yes/no question: 'Does this control exist and is it operating as designed?' By determining the sample size and evaluating test results statistically, auditors can reach defensible conclusions about control effectiveness.
ScholarGateНабор данных
  1. v1
  2. 2 Источники
  3. PUBLISHED
  1. v1
  2. 2 Источники
  3. PUBLISHED

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ScholarGateСравнение методов: Monetary Unit Sampling · Attribute Sampling in Auditing. Получено 2026-06-15 из https://scholargate.app/ru/compare