## Factors Influencing Sample Size in Tests of Controls

Sample size in Tests of Controls is driven by deviation rates (not monetary amounts):

Factor	Change	Effect on Sample Size
(a) Tolerable Level of Deviation	↑ Increases	↓ Decreases
(b) Expected Rate of Deviation	↑ Increases	↑ Increases
(c) Reliance on operating effectiveness of the control	↑ Greater reliance	↑ Increases
(d) Desired level of assurance that actual deviation does not exceed tolerable level	↑ Higher assurance	↑ Increases
(e) Number of sampling units in population	↑ or ↓	Negligible effect

### Key Insights

Tolerable deviation vs. expected deviation: If you expect deviations to be close to your tolerable limit, you need more evidence — so sample size increases.
Greater reliance on a control → bigger sample: The more you depend on a control to reduce substantive testing, the more evidence you need that it is actually working.
Population size: Again negligible — same principle as Tests of Details.

### Contrast with Tests of Details

Tests of Controls use rates (deviation rate); Tests of Details use monetary amounts (misstatement amounts).
Both share the negligible-population-size principle and the inverse relationship with tolerable thresholds.

Worked example

### Example 1

Example — Greater reliance on control:

The auditor decides to rely heavily on the automated 3-way matching control (PO vs. GRN vs. Invoice) to reduce the sample for substantive testing of purchases. Because reliance is high, the auditor must test a larger sample of the control's operation (e.g., 60 items instead of 25) to gain sufficient confidence that the control is functioning effectively.

### Example 2

Example — Expected rate near tolerable rate:

Tolerable deviation rate = 5%. Auditor expects deviation rate = 4% (close to limit). This narrow margin means a larger sample is needed to distinguish whether actual deviation truly stays below 5%.

⚠️ Common exam mistakes

Applying monetary-amount logic (from Tests of Details) to Tests of Controls — controls testing is about rates, not rupee amounts.
Forgetting that greater reliance on a control increases (not decreases) the required sample for that control test.
Assuming that if few deviations are expected, a very small sample is always acceptable — if expected deviation is close to tolerable deviation, sample size must increase.

Reference:

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Factors Influencing Sample Size — Tests of Controls

Worked example

⚠️ Common exam mistakes