## Why Sampling Instead of 100% Checking?

Even 100% checking does not yield absolute satisfaction — human fatigue, repetition, and error mean full checking is not foolproof
Statistical theory shows a randomly selected sample reveals the features and characteristics of the population — this is a mathematical truth
Auditors can derive the same quality of satisfaction from a much smaller sample as from checking all transactions

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## How Sampling Fits the Audit Workflow

```

Test of Controls (TOC)

↓

Assess operating effectiveness of controls (using sample)

↓

Design sample size for Tests of Details (TOD)

↓

Strong controls → smaller TOD sample

Weak controls → larger TOD sample

```

> Sampling is not a shortcut — it is a scientifically valid technique grounded in probability theory that allows the auditor to form a defensible conclusion about the entire population.

Worked example

### Example 1

A company processes 10,000 purchase transactions per month. Using random sampling, 150 items are selected. Sample shows 2 deviations (1.3%). Tolerable rate = 5%. Auditor concludes controls are operating effectively — without checking all 10,000. The random selection ensures the 1.3% finding is representative of the full population.

⚠️ Common exam mistakes

Believing 100% checking guarantees correctness — even full checking carries human error risk and does not give absolute assurance
Viewing audit sampling as merely a time-saving shortcut rather than a scientifically grounded technique
Not linking TOC conclusions to TOD sample size decisions — this is the key practical linkage in the audit process

Reference: — SA 530 – Audit Sampling

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Rationale for Sampling over 100% Checking

Worked example

⚠️ Common exam mistakes