# Statistical Advisor, Continuous vs Categorical Data

In general, variables (and data) either represent measurements on some continuous scale, or they represent information about some categorical or discrete characteristics.

For example, the weight, height, and age of respondents in a survey would represent continuous variables; in industrial or medical applications, survival/failure times are also continuous variables. However, a person's gender, occupation, or marital status are categorical or discrete variables: either a person is male or female, never married, married, or divorced, etc.

Some variables could be considered in either way. For example, a person's rating of someone else's attractiveness on a 4 point scale may be considered a continuous variable, or we may consider it a discrete variable with 4 categories.

Time series data are usually collected for continuous variables, over time. For example, stock quotes for a particular stock over successive trading days represent a time series of data.