Categorical data is a fundamental type of data in statistics that represents categories or groups. Unlike numerical data, categorical data classifies observations into distinct, non-overlapping groups based on qualitative characteristics. The values are labels or names rather than numerical measurements, and they describe qualities or attributes that cannot be ordered mathematically.
There are two main types of categorical data. First is nominal data, which has no natural order or ranking. The categories are simply different from each other, like gender, color, or brand names. Second is ordinal data, which has a natural order or ranking. These categories can be arranged in a meaningful sequence, such as education levels or satisfaction ratings from poor to excellent.
Categorical data appears everywhere in real life. In demographics, we see age groups, gender, and marital status. In education, we have degree levels and majors. Businesses use product categories and customer segments. Survey data often includes yes or no responses and rating scales. Medical fields use blood types and diagnosis categories. These examples show how categorical data helps us organize and classify information in meaningful ways.
Analyzing categorical data requires specific methods since we cannot use traditional mathematical operations like calculating means. Common analysis methods include frequency tables to count occurrences, bar charts for visual representation, pie charts to show proportions, and finding the mode or most frequent category. Cross-tabulation helps analyze relationships between different categorical variables. However, categorical data has limitations - we cannot calculate mean or median values, and mathematical operations are restricted compared to numerical data.
To summarize what we have learned about categorical data: It represents categories or groups based on qualitative characteristics. There are two main types - nominal data with no natural order, and ordinal data with meaningful ranking. Categorical data is everywhere in real life, from demographics and surveys to business and medical applications. We analyze it using frequency tables, charts, and mode calculations, but we cannot use traditional mathematical operations like calculating means or medians.