Advanced Audit and Assurance (International) · Evidence and testing considerations

Audit Sampling and Selecting Items for Testing (ISA 530)

Updated 11 October 2026

ISA 530 covers audit procedures applied to less than 100% of items in a population relevant to the audit, so that every item has a chance of selection. You design the sample, select items, test them, project errors to the population and conclude whether the population is acceptable. Tolerable misstatement drives the decision.

Understand Audit Sampling and Selecting Items for Testing (ISA 530)

Auditors rarely test every item. They test a sample and draw a conclusion about the whole population. ISA 530 sets out how to do this so the conclusion is reliable. Sampling applies when you want a conclusion on a population, such as tests of controls or tests of details. The population must be relevant to the audit objective. Sampling does not apply when you test 100% of items or select only specific items.

There are two approaches. Statistical sampling uses random selection and probability theory to evaluate results and measure sampling risk. Non-statistical sampling relies on judgement for size, selection or evaluation. Either can give sufficient appropriate evidence if the sample is representative. The key test is random selection plus mathematical evaluation: a sample needs both to be statistical.

Sampling risk is the risk that your conclusion from the sample differs from the conclusion if the whole population were tested. For tests of controls, it includes the risk of assessing control risk too low. For tests of details, it includes the risk of concluding there is no material misstatement when there is one. Non-sampling risk covers everything else, such as using wrong procedures or misreading evidence. Increasing sample size does not reduce non-sampling risk. Good planning, supervision and review do.

Two levels matter in tests of details. Tolerable misstatement is a monetary amount you set, at or below performance materiality, for which you seek assurance that it is not exceeded in the population. Tolerable rate of deviation is the equivalent for tests of controls. If projected misstatement plus any other errors exceeds tolerable misstatement, you do not accept the population without further work. If it is close, there is unacceptable sampling risk, so consider extending testing or asking management to adjust.

Sample design must be linked to the audit objective and the assertion tested. Define the population and check it is complete. Decide what counts as an error or deviation. Then choose size and selection method. Each item in the population must have a chance of selection. Where an item cannot be tested, for example a missing document, use alternative procedures. If you cannot, treat it as an error.

Key rules to remember

Projected misstatement (ratio method): Projected misstatement = (Misstatement found ÷ Sample value) × Population value; Use when errors are roughly proportional to item size. Compare the result, plus anomalies and other known errors, with tolerable misstatement.
Average error (simple extrapolation): Projected misstatement = (Misstatement found ÷ Number of items in sample) × Number of items in population; Use when errors are not tied to the size of the item. This is simple extrapolation of the average error per item in the sample.
Deviation rate: Sample deviation rate = Deviations found ÷ Sample size; Used for tests of controls. Compare with tolerable rate of deviation.
Evaluation rule: Compare projected misstatement (plus anomalies and other known misstatements) with tolerable misstatement; If projected misstatement exceeds tolerable misstatement, the population does not exhibit acceptable sampling risk, so do not accept it without further work. If it is close, evaluate whether sampling risk is unacceptable and consider extending testing or seeking adjustment.
Factors increasing sample size: Larger sample if: lower tolerable misstatement, higher assessed risk of material misstatement, higher expected misstatement, greater reliance on the test, no stratification of a variable population; A general direction rule, not an exact calculation.

How to solve Audit Sampling and Selecting Items for Testing (ISA 530) questions

Use this method for any ISA 530 question, whether it asks you to design a sample, explain terms or evaluate results.

1Identify the objective and the assertion being tested, and whether it is a test of controls or a test of details.
2Define the population and confirm it is complete and relevant to the objective. Define what counts as an error or deviation.
3Decide whether sampling is appropriate. Consider 100% testing or specific items where the population is small or high risk.
4Choose the approach and sample size using the factors: tolerable misstatement, assessed risk, expected errors, population size and stratification.
5Select items so each has a chance of selection. Name the method, such as random, systematic, monetary unit or haphazard, and explain why it fits.
6Perform procedures. Treat untested items using alternative procedures, or count them as errors.
7Project errors to the population, add anomalies and other known misstatements, and compare with tolerable misstatement. If the total exceeds it, or is close, evaluate whether sampling risk is unacceptable and consider extending testing. Investigate the cause of errors and anomalies.
8Conclude whether the evidence is sufficient and appropriate. If not, extend the sample, test other procedures, or request adjustment.

Quickest way: Four-line sampling answer

When to use it: Use in time-pressured questions asking you to evaluate results or suggest a sampling approach for a scenario.

Write the objective and tolerable misstatement in one line.
Calculate projected misstatement with a clear formula and show the figure.
Compare to tolerable misstatement and state the conclusion in one sentence.
Add one action: extend sample, investigate causes, or request adjustment. Link it to the scenario facts.

Common mistakes in Audit Sampling and Selecting Items for Testing (ISA 530)

Calling any sample with judgemental selection statistical.
Students think a large sample is automatically statistical.
Fix: Statistical needs random selection and probability-based evaluation. If either is missing, it is non-statistical.
Treating an error in the sample as the full misstatement.
Students stop after finding the error amount.
Fix: Project the error across the population, then compare the projected figure with tolerable misstatement.
Ignoring the causes of errors.
Students focus only on numbers.
Fix: Analyse nature and cause. An anomaly may be isolated, but a pattern, such as fraud or a system fault, may need wider work.
Confusing sampling risk with non-sampling risk.
Both are described as risks of wrong conclusions.
Fix: Sampling risk comes from testing only part of the population and can be reduced by a larger sample. Non-sampling risk comes from other causes such as wrong procedures.
Stating a larger sample is needed when risk falls.
Direction of factors is learned loosely.
Fix: Higher risk, higher expected errors and lower tolerable misstatement all increase the sample size. Lower risk reduces it.
Using sampling for items that should be tested individually.
Students apply sampling to every population.
Fix: Consider testing all high-value or unusual items separately, then sample the remainder.

Worked examples

Example 1

An auditor tests trade receivables with a book value of $4,000,000. A sample of items with book value $500,000 is tested and overstatements of $15,000 are found. Tolerable misstatement is $90,000. Errors are proportional to item size. Evaluate the result.

Show the solution

Use the ratio method as errors relate to size: $15,000 ÷ $500,000 = 3%.
Apply to the population: 3% × $4,000,000 = $120,000 projected misstatement.
Compare: $120,000 exceeds tolerable misstatement of $90,000.
Conclude the population cannot be accepted on this evidence, as the projected error is already above the tolerable level before any allowance for sampling risk.

Answer: Projected misstatement is $120,000, above tolerable misstatement of $90,000. The auditor should investigate causes, extend testing and ask management to adjust the balance if needed.

Example 2

Distinguish statistical from non-statistical sampling and recommend, with reasons, a selection method for testing 5,000 sales invoices for occurrence, where invoices are sequentially numbered and there are no concerns about patterns in the numbering.

Show the solution

Define statistical sampling: random selection and probability theory used to evaluate results, including sampling risk.
Define non-statistical sampling: judgement used in size, selection or evaluation. It can still provide sufficient appropriate evidence if the sample is representative.
Recommend random selection using computer-generated numbers. Every invoice has a known chance of selection, so it suits a large population and gives a defensible base for evaluation.
Note that a systematic method with a random start is acceptable if the population is sequentially ordered with no pattern.
Avoid haphazard selection if bias is a concern, as it can reflect auditor preference.

Answer: Statistical sampling needs random selection and probability-based evaluation. Non-statistical sampling relies on judgement. For the invoices, use random selection, using a computer generator, so each item has a chance of selection and the results can be projected.

Exam tips

Always show a projected misstatement figure and compare it to tolerable misstatement. A bare comment on error loses technical marks.
Tie the sampling method to the scenario facts, such as population size, ordering or risk, to earn professional skills marks.
When asked about factors affecting sample size, state the direction of each factor and explain why.
Mention alternatives to sampling, such as 100% testing, key item testing and analytical procedures, when the population is small or risky.
If errors found look like fraud, say so. Do not treat them as isolated, and consider ISA 240 responses.

Practice questions from Evidence and testing considerations

Audit Sampling and Selecting Items for Testing (ISA 530): frequently asked questions

What is the difference between statistical and non-statistical sampling?

Statistical sampling uses random selection and probability theory to evaluate results and measure sampling risk. Non-statistical sampling relies on auditor judgement. Both can give valid evidence if the sample is representative.

What is tolerable misstatement in ISA 530?

It is a monetary amount set by the auditor, at or below performance materiality, for a specific sampling procedure. If the projected misstatement exceeds it, the population is not accepted without further work.

How do you project errors found in a sample?

Extrapolate the sample misstatement to the population. Use the ratio method when errors scale with value, or the average method when they do not. Then compare the projection with tolerable misstatement.

What happens if an item selected cannot be tested?

Perform alternative procedures to get the same evidence, for example inspecting later cash receipts. If that is not possible, treat the item as an error or deviation.