Different summary measures are appropriate for different types of data, depending on the level of measurement:

Categorical. Data with a limited number of distinct values or categories (for example, gender or marital status). Also referred to as qualitative data. Categorical variables can be string (alphanumeric) data or numeric variables that use numeric codes to represent categories (for example, 0 = Unmarried and 1 = Married). There are two basic types of categorical data:

- Nominal. Categorical data where there is no inherent order to the categories. For example, a job category of "sales" isn't higher or lower than a job category of "marketing" or "research."

- Ordinal. Categorical data where there is a meaningful order of categories, but there isn't a measurable distance between categories. For example, there is an order to the values high, medium, and low, but the "distance" between the values can't be calculated.

Scale. Data measured on an interval or ratio scale, where the data values indicate both the order of values and the distance between values. For example, a salary of $72,195 is higher than a salary of $52,398, and the distance between the two values is $19,797. Also referred to as quantitative or continuous data.