Nominal data can be categorized into which types?

Nominal data refers to categorical data that can be classified into distinct categories without any intrinsic ordering. The classification of nominal data can be broken down into dichotomous and polychotomous types:

Dichotomous data represents two categories or groups, such as "yes" or "no," "male" or "female," where each category is mutually exclusive.

Polychotomous data, on the other hand, encompasses three or more categories. For instance, classifications like blood type (A, B, AB, O) fall under polychotomous data because they contain multiple categories that cannot be ordered or ranked.

This understanding is foundational in fields such as statistics and data analysis, especially in oncology where various patient demographics, disease types, and treatment responses are classified. Recognizing the distinction between these types of nominal data aids in effective data collection and analysis, allowing for appropriate statistical applications and interpretations.

The other options pertain to different types of data: interval and ratio represent ordinal relationships and metrics with meaningful zero points, while skewed and bimodal describe distributions of data rather than categories. Linked and delinked relate to connections in data sets but do not characterize nominal data types.