What characteristic of polychotomous data differentiates it from dichotomous data?

Polychotomous data is characterized by the ability to break into more than two categories, thereby providing multiple outcomes for a given variable. This characteristic is what clearly sets it apart from dichotomous data, which is limited to only two potential outcomes, such as "yes/no" or "true/false."

In the context of research and analysis, polychotomous data allows for more nuanced responses and a richer dataset, which can be important in studies that require categorization beyond a binary framework. For example, survey questions that ask respondents to select their favorite type of cancer treatment might include options like chemotherapy, radiation, immunotherapy, and surgery, reflecting multiple categories for analysis.

The other options do not capture the essence of what differentiates polychotomous data. For instance, the notion of representing numerical values does not specifically relate to the category structure of the data, as both types could potentially involve numerical representation in certain contexts. Furthermore, although polychotomous data could involve discrete data points, the distinctive feature is the number of categories available for choices.