A good audit sample is a representative subset of data that enables an auditor to draw reasonable conclusions about a whole population, balancing efficiency with risk. It should focus on high-risk areas, include a mix of items, and be large enough to provide confidence, often ranging from 10% of records to 30 items for frequent transactions.
A 'snapshot' sample is usually sufficient for process-based audit, roughly 20-50 cases.
The 5 Cs of audit (Criteria, Condition, Cause, Consequence, Corrective Action) are a framework for structuring clear, actionable audit findings, explaining what should be (Criteria), what is found (Condition), why it happened (Cause), what the impact is (Consequence/Effect), and how to fix it (Corrective Action/Recommendation) to drive organizational improvement and compliance.
Sample Selection
Therefore, all items in the population should have an opportunity to be selected. Random-based selection of items represents one means of obtaining such samples. Ideally, the auditor should use a selection method that has the potential for selecting items from the entire period under audit.
Audit sampling is an investigative tool in which less than 100% of the total items within the population of items are selected to be audited. It is an auditing technique that provides supporting evidence that allows auditors to issue audit opinions without having to audit every single item and transaction.
Statistical sampling requires that sample items are selected at random so that each sampling unit has a known chance of being selected. The sampling units might be physical items (such as invoices) or monetary units. With non-statistical sampling, an auditor uses professional judgment to select the items for a sample.
Yes, a 10% sample size is often acceptable and a good rule of thumb, especially for large populations, because it generally maintains the independence assumption for statistical tests and provides decent accuracy, though a smaller margin of error or high-stakes decisions might need more, while a very small population might need to survey everyone. It's a balance between precision, cost, and population size; a sample around 10% of the population (up to about 1000) is often sufficient, even if the population is huge, as results stabilize.
Audit sampling is the selection and evaluation of less than 100 percent of the population, which the auditor expects to be representative of the population and will provide a reasonable basis for conclusions about the population.
3.1. Probability sampling methods
Fundamental Principles Governing an Audit:
Big Five
The Five Star Audit process involves an in-depth examination of an organisation's Process Safety Management system(s) and associated arrangements. The audit focuses on the key aspects of managing process safety risks and offers a structured path for continual improvement towards best practice status.
A good maximum sample size is usually 10% as long as it does not exceed 1000. A good maximum sample size is usually around 10% of the population, as long as this does not exceed 1000. For example, in a population of 5000, 10% would be 500. In a population of 200,000, 10% would be 20,000.
Some balances, such as debt, may be tested 100% but more often, such as is the case with accounts receivable, the auditor will use sampling applications to obtain sufficient evidence to support the opinion and will not test 100% of the population.
The prominent 10-times rule suggests that the minimum sample size should be 10 times the maximum number of arrowheads pointing at a latent variable anywhere in the partial least squares path model. Despite its prominence in research practice, this rule of thumb lacks systematic validation.
CHOOSING SAMPLE SIZES – THE SCIENTIFIC APPROACH
Sample size calculations depend on four variables: • Size of population. Degree of accuracy required. Degree of confidence required. How often you expect your audit criteria to be met.
Acceptance sampling uses statistical sampling to determine whether to accept or reject a production lot of material. It has been a common quality control technique used in industry. It is usually done as products leave the factory, or in some cases even within the factory.
The golden rule is: the larger your sample size, the more reliable and valid your results are likely to be.
There is no universal agreement, and it remains controversial as to what number designates a small sample size. Some researchers consider a sample of n = 30 to be “small” while others use n = 20 or n = 10 to distinguish a small sample size. “Small” is also relative in statistical analysis.
Sampling without replacement results in trials that are not independent, but the 10% rule states that if the sample size is less than or equal to 10% of the population size, then the trials can be treated as if they are independent.
A good, robust and usable research sample is characterised by 4 key pillars:
Quality Audit Checklist. To conduct an effective quality audit, auditors often rely on a checklist that outlines the key areas to be assessed and the criteria for evaluation. A well-structured audit checklist ensures that auditors cover all relevant aspects during the audit process.
Since in many cases it is economically impractical to audit all transactions, the CDTFA encourages the use of sampling whenever feasible. There are generally two methods of sampling: judgment sampling and statistical sampling.