Continuous data can be categorized into two types. What are they?

Continuous data is typically categorized into two primary types: interval and ratio data. This classification is crucial because it helps determine the appropriate statistical analyses that can be conducted on the data.

Interval data refers to continuous data where the differences between values are meaningful, but there is no true zero point. For example, temperature measured in Celsius or Fahrenheit is interval data because the difference between values is consistent, but zero degrees does not represent a total absence of temperature.

Ratio data, on the other hand, includes all the properties of interval data but also has a true zero point, allowing for meaningful comparisons of magnitude. Examples include height, weight, and age, where a value of zero indicates a complete absence of the quantity being measured.

Understanding the distinction between these two types is fundamental in research, as it influences how data can be analyzed and interpreted, making this categorization pivotal in the study of statistics and various fields, including oncology research.

Nominal and ordinal data describes categorical data rather than continuous data, while qualitative and quantitative pertains to the nature of the data (descriptive versus numerical) rather than its continuous type. Bimodal and skewed refer to specific characteristics of data distributions rather than types of continuous data.