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Quantitative data refers to numerical information that can be measured and expressed mathematically. This type of data allows for statistical analysis and can be used to calculate averages, variances, and other statistical measures. Examples include measurements like height, weight, and platelet counts, which can take on a range of values.

Categorical data represents characteristics or qualities that can be divided into distinct groups or categories. Unlike quantitative data, it does not involve numerical values but rather labels or names that describe attributes. Examples include gender, blood type, or types of diseases, where the data can be classified but not measured numerically.

Data classification is the process of organizing data into categories based on shared characteristics. In statistics, understanding whether data is quantitative or categorical is crucial for selecting appropriate analytical methods. This classification helps in determining how to visualize the data, perform statistical tests, and interpret results effectively.