Which type of statistical data measures variables with observable order but unknown distances?

The correct answer is indeed ordinal data. This type of statistical data categorizes variables based on their relative order or rank, reflecting how items compare in magnitude or quality, but it does not provide precise information regarding the difference in size between those ranks.

In ordinal data, while you can say that one value is greater or lesser than another, the exact distance between these values is not quantifiable. For example, consider a ranking of customer satisfaction scores that might categorize feedback as “poor”, “average”, and “excellent.” We can determine that “average” is better than “poor” and worse than “excellent,” but the specific difference in satisfaction levels between these ranks is not clearly defined.

This distinguishes ordinal data from other types. Nominal data refers to categories without any order (like gender or color), while interval data has ordered values with measurable distances but lacks a true zero point (like temperature in Celsius). Ratio data includes order, measurable distances, and a meaningful zero point, allowing for comparisons of magnitude. In summary, ordinal data effectively conveys ranked information without specifics on the spaces between the ranks.