Data that has a *ratio scale *is data where it is meaningful to compute ratios. Returning to the temperature data discussed in the article on Interval data, with Fahrenheit readings we cannot meaningfully compute ratios. For example, 39 degrees is not in a useful way 39/31 times hotter than 31 degrees.

By contrast, if five people consumed 10, 20, 5, 3, and 1, cans of Coke in the last week, we can say that the second person consumed twice as much as the first person. The ability to compute ratios is the defining property of ratio scale data. An alternative way to understand this is that if the value of 0 truly means the absence of everything, then the data has ratio scale properties. For example, "0 cans of coke were drunk" means that no coke was drunk, whereas a temperature measurement of 0 degrees does not mean that there was no temperature.

It is important to appreciate that the data measurement scales relate to the properties of the original data itself and that different scale properties apply to calculations performed on that data. For example, in the case of the nominal data, although the data is nominal, the counts of the number of people to choose each color are ratio-scaled, and thus we can say that twice as many people preferred blue to pink.

Ratio data is occasionally referred to as being *scale *data.

The distinction between ratio and interval data tends to only relate to interpretation, and rarely has a big impact on the choice of analysis technique, so it is often useful to ignore the distinction between ratio and interval-scale data and refer to both as being *numeric.*

## See also

Overview of Data Types

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