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Microlesson · 5-min read

SA-530: Audit Sampling

## SA-530: Audit Sampling

### Introduction

  • Traditional 100% checking is economically wasteful and still cannot give absolute satisfaction
  • Statistical theory: a randomly drawn sample reveals the characteristics of the population
  • Sampling is not mandatory; extent of checking is the auditor's judgment (no statute specifies it)
  • Auditor should prefer statistical sampling as it is more scientific

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### Sampling Risk

Risk TypeIn Test of ControlsIn Test of DetailsEffect on Audit
Over-relianceControls appear more effective than they areMisstatement appears absent but actually existsInappropriate audit opinion (affects effectiveness)
Under-relianceControls appear less effective than they areMisstatement appears present but actually does not existInefficient audit (affects efficiency)

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### Key Definitions

TermMeaning
SamplingApplying audit procedures to less than 100% of items; all units have equal chance of selection
Sampling UnitIndividual items making up the population; conclusions drawn from the sample are projected to the entire population
AnomalyA misstatement/deviation NOT representative of the rest of the population
Tolerable Rate of MisstatementMonetary amount set by auditor — actual misstatement in the population should not exceed this
Tolerable Rate of DeviationRate of deviation from a prescribed control — actual deviation should not exceed this
StratificationDividing a population into sub-populations with similar characteristics
Non-Sampling RiskRisk of erroneous conclusion for reasons other than sampling (e.g., inappropriate procedure, misinterpretation of evidence)
PopulationEntire set of data from which the sample is selected and about which the auditor wishes to draw conclusions

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### Characteristics of Population

CharacteristicRequirement
CompletenessMust contain all items of the financial reporting period
AppropriatenessMust be relevant to the specific audit objective (e.g., credit sales → examine debtors)
ReliabilityMust contain supporting documents; unreliable population = irrelevant population

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### Statistical vs. Non-Statistical Sampling

FeatureNon-StatisticalStatistical
BasisKnowledge and experience of auditorRandom selection + probability theory + mathematical methods
BiasRisk of personal biasNo personal bias
ObjectivityNeither objective nor scientificMore objective and defensible
Best suited forSmaller organizations; year-end transaction checksLarge organizations with huge transaction volumes
AdvantageSimple to operateMinimum sample size; calculated risk; better description of large data
DisadvantageSample quality cannot be measuredNot always appropriate (e.g., if staff lacks knowledge)

Statistical sampling may NOT be appropriate when:

  • Another approach provides satisfactory information with less effort
  • Audit staff lacks knowledge of the sampling technique

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### Methods to Collect Sample

MethodKey Features
Simple RandomRandom Number Table/Generator; every item has equal chance; suitable for homogeneous populations
Stratified RandomExtension of simple random; heterogeneous population divided into homogeneous strata; reduces sample size without increasing sampling risk; results projected per stratum
Interval (Systematic)Population ÷ sample size = sampling interval; e.g., 500 vouchers ÷ 50 = every 10th item; multiple starting points recommended to reduce variability
Monetary Unit (MUS)Value-weighted selection; auditor's efforts directed toward high-value items; conclusion expressed in monetary amounts
BlockContinuous range of transactions selected randomly; risk — if management knows the selection pattern, misstatements in other blocks may be overlooked
HaphazardNo structured technique; auditor's bias cannot be fully avoided; NOT appropriate for statistical sampling

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### Factors Affecting Sample Size

FactorEffectApplies in TOCApplies in TOD
Higher RMMIncreases sample size
Higher tolerable rateDecreases sample size
Higher expected rate (deviation/misstatement)Increases sample size
Higher desired assuranceIncreases sample size
Greater reliance on other substantive proceduresDecreases sample size
Appropriate stratificationDecreases sample size
Change in population sizeNegligible effect

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### Audit Procedures and Projecting Misstatements

Steps when a selected item cannot be tested:

1. Apply alternative procedures on a replacement item (e.g., if sale document is lost, obtain DC from debtor under SA-505; if no reply, examine subsequent cash receipts)

2. If neither procedure nor alternative can be applied → treat as: Misstatement (TOD) or Deviation (TOC)

Projecting Misstatements:

  • Exclude anomalies from the main projection
  • However, the effect of anomalous misstatements must be added back to the projection of non-anomalous misstatements
  • TOD: Auditor SHALL project misstatements to the population
  • TOC: No explicit projection of deviations is necessary

Nature and Cause of Misstatements/Deviations:

  • If misstatements share a common feature, extend procedures to all transactions with that feature
  • Intentional misstatement/deviation = fraud
  • For anomaly: obtain high degree of certainty + perform additional procedures to confirm it does not affect the remainder of the population

Worked example

### Example 1

Interval Sampling: An auditor needs to check 50 vouchers from a population of 500. Sampling interval = 500 ÷ 50 = 10. The auditor selects a random start (say, voucher #3) and then checks every 10th item: 3, 13, 23, 33 ... The auditor uses multiple starting points to reduce the risk of missing concentrated errors.

### Example 2

Stratified Random Sampling: A population of 1,000 debtors has 50 with balances above ₹10 lakhs and 950 with balances below ₹1 lakh. Instead of treating the population as homogeneous, the auditor stratifies: 100% coverage of the top 50 debtors (high-value stratum) and a random sample of 30 from the remaining 950 (low-value stratum). This reduces sample size without increasing sampling risk.

### Example 3

Projecting Misstatements (Anomaly Rule): The auditor finds 3 misstatements in a sample: two due to a systematic clerical error (total ₹50,000), and one unique misstatement of ₹2,00,000 caused by a one-off data entry error (anomaly). The anomaly is excluded from the projection of the systematic errors. However, the ₹2,00,000 anomaly effect is still added back. Final projected misstatement = (projected systematic error) + ₹2,00,000.

⚠️ Common exam mistakes

  • Using Haphazard sampling for statistical sampling — haphazard sampling is NOT appropriate for statistical sampling because auditor bias cannot be fully avoided.
  • Forgetting to add back anomaly effect after exclusion from projection — the effect of anomalous misstatements must still be added to the overall projected misstatement figure.
  • Applying explicit deviation projection for TOC — unlike TOD, no explicit projection of deviations is required for Tests of Controls.
  • Assuming sample size always increases with population size — change in population size has a negligible effect on sample size; this is a key statistical property.
  • Confusing 'Tolerable rate of deviation' (for TOC) with 'Tolerable rate of misstatement' (for TOD) — they measure different things (deviation from control vs. monetary misstatement).
  • Treating Block sampling as a strong method — if management can anticipate the block selection, they can manipulate items outside the block, making this method vulnerable.
Reference: SA-530 — Standards on Auditing — ICAI
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