The basic assumption underlying the use of analytical procedures is :

 Options:

a) It helps the auditor to study relationship among elements of financial information b) Relationship among data exist and continue in the absence of known condition to the contrary c) Analytical procedures will not be able to detect unusual relationships d) None of the above.

The Correct Answer Is:

b) Relationship among data exist and continue in the absence of known condition to the contrary

Correct Answer Explanation: b) Relationship among data exist and continue in the absence of known condition to the contrary

Analytical procedures are a crucial aspect of auditing, aiming to evaluate financial information for potential inconsistencies or irregularities. The basic assumption underlying their use is that “relationship among data exist and continue in the absence of known condition to the contrary.”

This means that auditors rely on the continuity and consistency of relationships between financial data unless evidence suggests otherwise. This assumption forms the basis for conducting analytical procedures effectively.

The assumption that “relationship among data exist and continue in the absence of known condition to the contrary” underpins the effectiveness of analytical procedures in auditing. Auditors depend on this foundational principle to assess financial information accurately.

By assuming the continuity of relationships between financial elements, auditors can establish benchmarks and expectations for the data. This assumption guides auditors to identify potential discrepancies or irregularities that deviate from these expected relationships.

Consequently, it empowers auditors to discern unusual patterns, fluctuations, or inconsistencies, prompting further investigation into potential errors, fraud, or misstatements in the financial records. This fundamental assumption forms the cornerstone for the application and success of analytical procedures in the auditing process.

Now, let’s break down why the other options are not the correct underlying assumption for the use of analytical procedures:

a) “It helps the auditor to study relationship among elements of financial information.”

While analytical procedures do facilitate the examination of relationships among financial elements, this statement doesn’t encapsulate the underlying assumption. The primary assumption isn’t just about the ability to study relationships; it’s about presuming the continuity and persistence of these relationships unless contrary evidence is found.

Analytical procedures are not merely tools for examination; they rely on the belief that relationships among financial data persist unless proven otherwise, forming the basis for detecting anomalies or irregularities.

c) “Analytical procedures will not be able to detect unusual relationships.”

Contrary to this statement, analytical procedures are explicitly designed to unearth irregular or unexpected relationships within financial data. They leverage various techniques such as ratio analysis, trend analysis, and benchmarking to identify discrepancies or outliers that might signal potential issues.

The assumption isn’t that analytical procedures are incapable of detecting unusual relationships; instead, it’s about expecting the data to follow established patterns unless evidence suggests otherwise.

d) “None of the above.”

This option doesn’t offer the foundational assumption but instead dismisses the provided options without providing an accurate representation of the core assumption behind the use of analytical procedures.

It doesn’t address the continuity or persistence of relationships in financial data, which is fundamental to the application of analytical procedures in auditing.

In essence, the foundational assumption guiding the use of analytical procedures in auditing is centered on presuming the ongoing existence of relationships among financial data unless evidence suggests the contrary.

This assumption forms the basis for evaluating financial information and detecting potential irregularities or discrepancies that may indicate issues within the data.

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