What characterizes interval data scale?

Interval data is characterized by having equal intervals between values but lacking a true zero point. This means that the differences between values are meaningful and consistent, allowing for the comparison of distances between measurements. For example, in temperature measured in Celsius or Fahrenheit, the difference between 10 degrees and 20 degrees is the same interval as between 20 degrees and 30 degrees. However, there is no absolute zero in these scales that indicates a complete absence of the variable being measured (in the case of temperature, 0 degrees does not mean there is no temperature).

The presence of equal intervals allows for a range of statistical analyses that are appropriate for this level of measurement, such as calculating means and standard deviations. This is in contrast to other data scales: categorical scales do not have order, ordinal scales have ranked orders but not consistent intervals, and nominal scales consist of discrete categories without any inherent ranking or interval measurement.