Maths: How Data Types Structure Statistical Analysis

Every statistical investigation begins with a simple question: what kind of information are we actually collecting? Data falls into distinct types, and recognising them shapes everything that follows. Categorical data describes qualities or named groups, such as a favourite lunch option chosen from pizza, burger, or salad. Discrete data, by contrast, counts things in whole, indivisible units, like the number of meals purchased per week, which can only take values such as 0, 1, 2, 3, 4, or 5 — never a fraction of a meal. Continuous data, the third type, would allow any value within a range, measured rather than counted. Why does this classification matter? Because each data type supports different summaries and decisions. Categorical data reveals proportions and preferences, guiding what to stock; discrete data gives exact counts, guiding how much to order. Understanding these distinctions connects raw observations to meaningful analysis, forming the foundation for choosing appropriate graphs, averages, and interpretations throughout your studies.