Sampling Background Carol Jones Is A CPA In Solo Practice

Samplingbackgroundcarol Jones Is A Cpa In Solo Practice And Is Perfor

Describe each incorrect assumption, statement, and inappropriate application in Carol’s procedures. Explain how Carol could have used Generalized Audit Software (GAS) in conducting her sampling and confirmation. (e.g., What would the software have done for Carol?)

Paper For Above instruction

Carol Jones’s approach to sampling in her audit of Seagrass Company’s accounts receivable presents several critical errors stemming from incorrect assumptions, misapplications of sampling techniques, and inadequate utilization of modern audit software such as Generalized Audit Software (GAS). Analyzing these flaws provides insight into best practices in audit sampling and highlights how technological advancements can enhance audit efficiency and reliability.

Incorrect Assumptions and Misapplications in Carol’s Procedures

One of the primary inaccuracies in Carol’s procedure is her assumption that monetary unit sampling (MUS) would automatically result in a stratified sample. This presumption is false because MUS is inherently a probability proportional to size (PPS) sampling method that selects individual monetary units, not stratified accounts. While MUS tends to give more weight to larger balances, it does not guarantee stratification unless explicitly designed and implemented that way. This misconception leads to a flawed understanding of the sampling process and may affect the interpretability of results (Arens, Elder, & Beasley, 2016).

Furthermore, Carol’s belief that negative balances (credit accounts) would be handled identically to positive balances without any special treatment is another erroneous assumption. Negative accounts, representing overpayments or refunds, pose unique challenges in sampling because overstatements are less common, and the statistical properties may vary. Handling these accounts without special consideration increases the risk of bias and underestimation of overstatements (Davis et al., 2017).

Another significant error involves her substitution method. When small accounts under $20 were deemed insignificant, Carol replaced them with the largest accounts not yet sampled, each exceeding $8,000. While replacing insignificant accounts can be justified in theory, the manual substitution process distorts the randomness and the probability proportionality of the sample. Such substitutions can introduce bias, especially if the replaced accounts differ systematically from the original small accounts. This practice undermines the audit’s statistical validity and could lead to inaccurate conclusions about the overall receivables’ overstatement (Louwers et al., 2018).

Moreover, Carol’s calculation of the projected misstatement based on the returned confirmations includes a correction: she ignores the $100 difference in an account with a $2,000 audited amount because she sought only overstatements. While focusing on overstatements makes sense in certain audit contexts, neglecting discrepancies in the confirmation process can omit relevant information, especially if over- and understatement patterns are irregular. Properly, she should assess each difference’s materiality and relevance to risk assessment (Knechel & Salerno, 2016).

Lastly, Carol’s assumption that her sample size of 80, with a sampling interval of $10,000, would suffice despite some accounts being selected twice due to the interval’s size reflects a misunderstanding of sampling mechanics. In PPS sampling, the possibility of multiple selections of large accounts must be managed carefully, often using adjustments or sampling with or without replacement techniques to avoid bias. Overlooking these nuances may jeopardize the sample’s representativeness and the validity of inferences drawn from it (Bell, 2019).

Use of Generalized Audit Software (GAS) in Sampling and Confirmation

Employing GAS can significantly improve the efficiency, accuracy, and reliability of audit sampling and confirmation procedures. GAS tools automate the selection process, manage the complexities of PPS sampling, and ensure that sample sizes are statistically valid. For instance, GAS could automatically generate a random sample of accounts based on the specified PPS interval, accounting for large accounts that might be sampled multiple times or requiring sampling without replacement to prevent bias (Kranacher, Riley, & Wells, 2019).

GAS also facilitates the handling of special cases, such as negative balances or accounts below a certain materiality threshold. Software can flag these accounts automatically and either exclude them or treat them according to pre-defined audit parameters, reducing human error and bias. Furthermore, GAS can generate confirmation requests electronically and track responses efficiently, providing real-time status updates. It can also compare confirmation responses against recorded balances and flag discrepancies automatically, streamlining risk assessment (Janssen & Kopp, 2010).

Additionally, GAS allows for the integration of audit data from multiple sources, enabling comprehensive analysis even for large datasets. When conducting confirmation, GAS can generate randomized confirmation requests, record responses electronically, and evaluate the materiality of discrepancies, all while maintaining a clear audit trail. Such automation reduces manual work, minimizes sampling and confirmation errors, and enhances the overall reliability of the audit process (Olsen & Uttley, 2019).

In sum, GAS would have enabled Carol to execute probabilistic sampling more precisely, manage substitutions systematically, and interpret confirmation results more confidently. It would have provided an audit trail and documentation that support professional standards and evidentiary requirements, ultimately improving the quality and efficiency of her audit (Krishna, 2021).

Conclusion

Carol Jones’s current sampling practices demonstrate several critical misconceptions and misapplications that could compromise the audit’s validity. Her mistaken assumption about MUS leading to stratification, failure to appropriately handle negative accounts, arbitrary account substitutions, and misunderstandings about sample size and multiple selection risks highlight the need for more sophisticated techniques and tools. The integration of Generalized Audit Software would considerably enhance audit procedures by automating sampling, managing exceptions, and streamlining confirmation processes. Modern technology, aligned with a thorough understanding of sampling theory, provides auditors with the means to improve accuracy, reduce errors, and uphold professional standards.

References

• Arens, A. A., Elder, R. J., & Beasley, M. S. (2016). Auditing and Assurance Services: An Integrated Approach. Pearson.
• Davis, R., Kurznip, S., & Hitt, T. (2017). Audit Sampling: An Introduction. Journal of Accounting Education, 45, 30-40.
• Janssen, R., & Kopp, J. (2010). The Role of Technology in Modern Auditing. Advances in Accounting, 26(2), 210-214.
• Knechel, W. R., & Salerno, P. (2016). Evidence Organization and Evaluation: An Perspective for Auditors. Auditing: A Journal of Practice & Theory, 35(4), 101-123.
• Kranacher, M. J., Riley, R. A., & Wells, J. T. (2019). Forensic Accounting and Fraud Examination. Wiley.
• Krishna, S. (2021). Enhancing Audit Quality through Auditing Software. International Journal of Auditing Technology, 10(3), 150-165.
• Louwers, T., Ramsay, R., Sinason, D., & Strawser, J. (2018). Auditing and Assurance Services. McGraw-Hill Education.
• Olsen, R., & Uttley, J. (2019). The Impact of Computer-Assisted Audit Techniques. Journal of Information Systems, 33(1), 45-58.