When we place a series of categories in order in some continuum such as severity, occurrence, or detectability, we may represent this ordering with numbers. Such numbers are rankings. If we assign the value of 1 to the lowest ranked category in the continuum, then 1 is below 2, 2 is below 3, 3 is below 4, and so on. Values with this property of order are called “ordinal-scale data.” The rankings on severity, occurrence, and detectability are intended to be ordinal-scale data.

However, before the operations of addition and subtraction are meaningful, you absolutely and positively *must* have interval-scale data. Interval-scale data are data that possess both ordering and distance—not only is 1 less than 2, and 2 is less than 3, but also the distance from 1 to 2 is exactly the same as the distance from 2 to 3. It is this notion of distance that gives meaning to addition and subtraction. Without the metric imposed by distance, you are operating in Wonderland, where 1 + 2 is equal to whatever the Red Queen wants it to be today.

Before the operations of multiplication and division can be meaningful, you must have ratio-scale data. Ratio-scale data are data that posses ordering, distance, and an absolute zero point. A classic example of data that are interval-scale but not ratio-scale are temperatures in degrees Fahrenheit or Celsius. Since both of these scales use an arbitrary zero point, multiplication and division do not make sense. However, addition and subtraction do result in meaningful numbers. For example, in either system, the following is a true statement: 60° + 10° = 70° But in either system the following equation is nonsense: 60°/80° = 0.75