Which of these situations would most likely raise concerns about data completeness?

Choosing the situation involving a database of employee salaries that excludes senior executives highlights a critical aspect of data completeness. Data completeness refers to the extent to which all required data is present and accounted for. When certain key data points—such as salaries of senior executives—are missing from the database, it raises concerns because the dataset does not provide a full or accurate picture. This lack of representation can lead to biased analyses and decisions based on incomplete information.

In contrast, situations like mistyped inventory numbers or error messages in temperature records indicate issues of data accuracy or quality rather than completeness. These errors do not necessarily imply that key data are missing; rather, they suggest that the data that is present may be incorrect or unreliable. Thus, they do not directly address completeness, which is crucial for thorough decision-making processes.