*Binary *data is a special case of nominal data, which either:

- Contains exactly two unique values (e.g., 0 or 1, Male or Female, Taller or Shorter).
- Contains more than two categories, where:
- Two of the categories are interesting.
- The remaining categories will be treated as missing values when performing the analysis (e..g,
*Missing data, **Don't know, **Refused*).

Binary variables are also known as *boolean variables, dichotomous variables, **flags,* and *indicator variables*.

Binary data is particularly useful in data analysis because despite being a special case of nominal, it also has all the properties of ordinal and interval data. For example, consider a binary variable that contains a value of 0 for people that consumed no cans of Coca-Cola in the past week, and a value of 1 for people that consumed one or more cans of Coca-Cola in the past week. With such data:

- We can perform all the calculations that we would perform using nominal data (because it is technically nominal data).
- When binary data is coded as 1s and 0s, the average of the data is the same as the proportion of 1s in the data. This useful relationship can save a lot of time when computing summary statistics. Mor generally, many techniques developed for interval data are applicable to binary data (e.g., linear regression).
- Binary variables can be merged, using an
*or *operation.

## See also

Overview of Data Types

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