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Discrete data refers to countable data points that can only take specific values, often represented as whole numbers. Examples include the number of students in a classroom or the number of cars in a parking lot. Discrete data cannot be subdivided meaningfully, as it represents distinct and separate values.

Continuous data, in contrast to discrete data, can take any value within a given range and can be measured rather than counted. Examples include height, weight, and temperature, where values can be infinitely divided into smaller increments. Continuous data is often represented using real numbers and can include fractions and decimals.

Understanding the difference between discrete and continuous data is crucial for statistical analysis. For instance, the exact heights of NBA players are considered continuous data because height can vary and be measured in fractions of an inch. In contrast, the number of players on a team is discrete, as it can only be a whole number.