When an internal auditor calculates the mean (average), median (middle value), and range (difference between highest and lowest values) of a data population, the primary purpose is to assess the distribution of data and detect anomalies. Let’s analyze the answer choices:

Option A: To inform the classification of the data population.

Incorrect. Classification typically involves categorizing data into specific groups, which requires different statistical or analytical techniques like clustering or decision trees. Mean, median, and range are more useful for identifying distribution patterns.

Option B: To determine the completeness and accuracy of the data.

Incorrect. While summary statistics can highlight extreme values, completeness and accuracy are usually assessed through data reconciliation, validation checks, and comparison with source records.

Option C: To identify whether the population contains outliers.

Correct.

The range (difference between the largest and smallest values) helps to detect extreme values.

The mean and median can show whether the data is symmetrical or skewed (which may indicate outliers).

If the mean is significantly different from the median, it suggests potential outliers pulling the average in one direction.

IIA Reference: Internal auditors use data analytics to detect anomalies and potential fraud by identifying outliers. (IIA GTAG: Auditing with Data Analytics)

Option D: To determine whether duplicates in the data inflate the range.

Incorrect. Duplicates may affect the data set, but range calculations alone do not determine whether duplicates exist. Duplicate identification usually involves checking for repeated entries, not just extreme values.