Chapter 10. Identification and Data Assessment

In Chapter 10 the focus of the material is identifying and assessing data. One of the chief concerns of identifying and assessing data is extrapolation and interpolation. Please explain both of these concepts and give a reason why these scenarios would occur.     

 

Chief Concerns of Identifying and Assessing Data

          Identification and data assessment refers to the risk assessment procedures of material misstatement that are sufficient to provide a reasonable basis for identifying potential hazards that can cause harm. It also helps determine the appropriate solutions for eliminating the hazards or risk control methods if the hazards cannot be eliminated. Identification and data assessment may involve various risk assessment procedures. I.e., obtaining an understanding of the company and its environment, performing analytical methods, obtaining data information of the company's activities and engagement, and conducting a discussion regarding the risks of material misstatement with team member ("AS 2110: Identifying and assessing risks of material misstatement," 2018).

          One of the chief concerns of identifying and assessing data is the use of extrapolation and interpolation techniques. Extrapolation refers to the process of estimating a value based on extending the known factors beyond the area that is certainly known. Extrapolation is used to calculate unknown values beyond the given data points. It uses the function to predict the value of the dependent variable for an independent variable outside the data range ("Interpolation and extrapolation definition and differences," 2020). On the other hand, interpolation refers to the process of estimating unknown values from known values. Interpolation can predict the value of the dependent variable for an independent variable within the midst of the data.

          The phenomenon of the interpolation concept is that the process of data selection can be located within a particular range of information (Wahab, 2017). It occurs when two values are known, and there is a need to identify another value within the range. Interpolation is used in identification and data assessment and can be applied to various domains, i.e., business, science, technology, and education. In turn, the extrapolation concept implies that data should be selected from areas beyond the selected range. This means that the data being selected should exceed the most significant number in the identified data set or lower than the lowest limit number ("Interpolation and extrapolation definition and differences," 2020). Therefore, the values in extrapolation are determined before the statistical procedure being extended to allow correct guess of data.

         The scenarios of extrapolation and interpolation would occur because they are used in identifying and assessing the risks of material misstatement. The risk of the material may arise from external factors, i.e., company-specific factors and environmental conditions. They can also occur from internal control over financial reporting competencies. Therefore, extrapolation and interpolation techniques can be used by auditors to identify risks of material using the information obtained from the data assessment ("AS 2110: Identifying and assessing risks of material misstatement," 2018).

          The techniques can also be used in companies to evaluate the identified risks to determine whether they pervasively relate to the financial statements and how they can affect many assertions. The scenarios may occur in assessing the likelihood of misstatement by identifying the possibility of multiple misstatements and evaluating possible risks that could result in a material misstatement. In this case, the auditor should account for the planned degree of reliance on control measures selected for testing. Extrapolation and interpolation of data also occur when identifying significant accounts and disclosures together with their relevant assertions ("AS 2110: Identifying and assessing risks of material misstatement," 2018). This helps determine whether an account or disclosure is significant or if the assertion is relevant.

           The scenarios of extrapolation and interpolation would also occur when evaluating whether the data gathered from the assessment procedures indicate the presence of fraud risk factors. Fraud risk factors may not necessarily tell the existence of fraud, but they can help show circumstances in which fraud exists. In conclusion, it's advisable to use interpolation in assessing data because it has a greater likelihood of obtaining correct values hence a valid estimate, unlike when using the extrapolation technique.

Title: Identification and Data Assessment Outline

1. Introduction

          Identification and data assessment refers to the risk assessment procedures of material misstatement that are sufficient to provide a reasonable basis for identifying potential hazards that can cause harm.

2. Extrapolation and Interpolation Meaning

          Extrapolation refers to the process of estimating a value based on extending the known factors beyond the area that is certainly known. Extrapolation is used to calculate unknown values beyond the given data points.

       Interpolation refers to the process of estimating unknown values from known values. Interpolation can predict the value of the dependent variable for an independent variable within the midst of the data.

3. Why Extrapolation and Interpolation scenarios would occur

          The scenarios of extrapolation and interpolation would occur because they are used in identifying and assessing the risks of material misstatement.

          The techniques can also be used in companies to evaluate the identified risks to determine whether they pervasively relate to the financial statements and how they can affect many assertions.

          The scenarios of extrapolation and interpolation would also occur when evaluating whether the data gathered from the assessment procedures indicate the presence of fraud risk factors.

4. Conclusion

          In conclusion, it's advisable to use interpolation in assessing data because it has a greater likelihood of obtaining correct values hence a valid estimate, unlike when using the extrapolation technique.