Which characteristic defines mutually exclusive measures?

Mutually exclusive measures are characterized by the principle that each measure can only belong to one category at any given time. This means that a measure can be identified by one and only one attribute. In practical terms, if a data point is assigned to a particular category, it cannot simultaneously belong to another category. This is particularly important in research and data analysis, as it ensures clarity and precision in categorizing data.

For instance, if you were categorizing individuals based on their age group, someone who is categorized as "18-24" cannot also be simultaneously categorized as "25-34." This exclusivity is crucial for accurate data interpretation and analysis, as it prevents confusion and overlap among categories. The concept ensures that statistical analyses yield valid and reliable results, as each data point clearly corresponds to a single measure without ambiguity.

In contrast, the other options do not capture the intricacies of mutually exclusive measures; overlapping categories or multiple attributes create potential for confusion and misrepresentation in data categorization, which is contrary to the intent of establishing mutually exclusive classifications.