Discrete and continuous are terms used to describe types of data in statistics. Here are the main differences between discrete and continuous data:
Discrete Data:
- Refers to countable, individualized, and nondivisible figures in statistics.
- Exists only in set increments.
- Has clear spaces between values.
- Contains distinct or separate values.
- Can be counted.
- Is represented graphically using a bar graph.
Examples of discrete data include the number of employees in a department, the number of new customers signed on last quarter, and the number of products currently held in inventory.
Continuous Data:
- Refers to data that falls in a constant sequence.
- Is considered the complete opposite of discrete data.
- Refers to the unspecified number of possible measurements between two presumed points.
- Has values that are not fixed and have an infinite number of possible values.
- Can be measured.
- Includes any value within the preferred range.
- Is represented graphically using a histogram or line graph.
Examples of continuous data include the height or weight of a person, the temperature of a room, and the time it takes to complete a task.
Both types of data are important for statistical analysis, and its important to know how to interpret data. Valuable research and insights are made by combining both sets of data.
