a5c-ai Audit Sampling Calculator
Overview
The Audit Sampling Calculator skill provides comprehensive audit sampling capabilities for both statistical and non-statistical approaches. It supports sample size determination, selection, and evaluation across various audit contexts.
Capabilities
Attribute Sampling
- Sample size calculation
- Expected error rate input
- Tolerable deviation rate
- Confidence level selection
- Upper error limit calculation
- Pass/fail evaluation
Monetary Unit Sampling (MUS)
- Population stratification
- Sampling interval calculation
- Selection methodology
- Projected error calculation
- Upper error bound
- Tainting analysis
Classical Variables Sampling
- Mean-per-unit estimation
- Difference estimation
- Ratio estimation
- Standard deviation calculation
- Precision determination
- Confidence interval
Sample Size Calculation
- Risk model inputs
- Tolerable misstatement
- Expected misstatement
- Confidence factors
- Population characteristics
- Prior year results
Projection of Errors
- Known error projection
- Likely error calculation
- Sampling risk assessment
- Anomalous error treatment
- Extrapolation methods
- Documentation requirements
Confidence Level Analysis
- Risk of incorrect acceptance
- Risk of incorrect rejection
- Achieved precision
- Sample result evaluation
- Decision criteria
- Conclusion documentation
Usage
Substantive Testing Sample
Input: Population, materiality, risk assessment, expected error
Process: Calculate sample size, select items, project results
Output: Sample selection, projected misstatement, audit conclusion
Control Testing Sample
Input: Control population, tolerable rate, expected rate
Process: Determine sample, execute testing, evaluate results
Output: Upper deviation rate, control reliance conclusion
Integration
Used By Processes
- Internal Audit Planning and Execution
- SOX Compliance and Testing
- External Audit Coordination
Tools and Libraries
- IDEA
- ACL Analytics
- Statistical sampling libraries
- Audit workpaper platforms
Cross-Specialization Use
- QA Testing Automation
- Data Quality domains
Best Practices
- Document sampling methodology selection rationale
- Ensure random selection integrity
- Investigate all identified errors
- Consider qualitative factors in evaluation
- Maintain population completeness
- Archive sampling parameters for reference