Internal Audit Sample Size Calculator

Audit Sample Size Formula:

\[ n = \frac{N}{1 + (N - 1) \times (E / Z)^2} \]

Population (N):

items

Precision (E):

decimal

Z-Score (Z):

Sample Size (n):

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1. What is the Internal Audit Sample Size Calculator?

The Internal Audit Sample Size Calculator determines the appropriate sample size for audit testing based on population size, desired precision, and confidence level. This ensures audit procedures are both efficient and statistically valid.

2. How Does the Calculator Work?

The calculator uses the audit sample size formula:

\[ n = \frac{N}{1 + (N - 1) \times (E / Z)^2} \]

Where:

\( n \) — Required sample size
\( N \) — Total population size
\( E \) — Desired precision (margin of error)
\( Z \) — Z-score based on confidence level

Explanation: This formula adjusts for finite population correction, providing more accurate sample sizes for smaller populations commonly encountered in internal auditing.

3. Importance of Sample Size Calculation

Details: Proper sample size calculation ensures audit testing is statistically valid, provides adequate coverage of the population, and supports audit conclusions with appropriate confidence levels.

4. Using the Calculator

Tips: Enter the total population size, desired precision (typically 0.05 for 5% margin of error), and select the appropriate confidence level based on audit risk assessment.

5. Frequently Asked Questions (FAQ)

Q1: What confidence level should I use?
A: 95% confidence is standard for most audits. Use 90% for lower risk areas and 99% for high-risk or regulatory compliance audits.

Q2: How do I determine precision?
A: Precision represents the margin of error. Lower precision values (e.g., 0.05) provide more accurate results but require larger sample sizes.

Q3: What if my population is very large?
A: For populations over 10,000, the sample size becomes relatively stable and approaches the infinite population formula.

Q4: Can I use this for attribute sampling?
A: This formula is primarily for variables sampling. Attribute sampling requires different formulas based on expected error rates.

Q5: How should I select the sample?
A: Use random sampling methods to ensure each item has an equal chance of selection, maintaining statistical validity.