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.