Data Types: Categorical, Ordinal, Continuous, Binary

Before you can analyze data, you must understand what kind of data you have. This is not an abstract classification exercise — it directly determines which statistical tests are appropriate, how you summarize your results, and how you present them. Categorical (nominal) data consists of groups or categories with no natural order. Examples: blood type (A, B, AB, O), sex (male, female), diagnosis (pneumonia, asthma, COPD). You can count how many people are in each category, but you cannot rank them or calculate a meaningful average. Ordinal data consists of ordered categories where the intervals between categories are not necessarily equal. Examples: pain severity (mild, moderate, severe), education level (primary, secondary, university), tumor staging (I, II, III, IV). You can rank them, but the 'distance' between mild and moderate is not necessarily the same as between moderate and severe. Continuous data is measured on a numeric scale with meaningful intervals. Examples: blood pressure (120 mmHg), hemoglobin (13.5 g/dL), age (45 years), weight (72 kg). You can calculate means, standard deviations, and use parametric tests. Binary (dichotomous) data is a special case of categorical data with exactly two categories. Examples: alive/dead, positive/negative, cured/not cured. Binary outcomes are extremely common in clinical research. Why does this matter? Because statistical tests are designed for specific data types. A t-test compares means of continuous data. A chi-squared test compares proportions of categorical data. Using the wrong test for your data type produces meaningless results. In Level 6, you will learn exactly which test to use for each data type. For now, practice identifying data types — it is a skill you will use in every study you design or read.