Ratiodata

Ratiodata is a term used in data science to describe data elements that are measured on a ratio scale, a level of measurement in which both the differences between values and their ratios are meaningful, and where the zero point represents an actual absence of the quantity. In practice, ratiodata is typically numeric data accompanied by units and supports arithmetic operations such as addition, subtraction, multiplication, and division.

Key characteristics of ratiodata include a meaningful zero value and the ability to form true ratios between measurements. Because of these properties, many standard statistical operations—such as computing means, standard deviations, and coefficient of variation—are appropriate for ratiodata, and multiplicative comparisons (e.g., “twice as long,” “half as heavy”) are valid. Care is needed when applying transformations; some common transformations (for example, logarithms) require nonzero, strictly positive data.

Data modeled as ratiodata are commonly stored as numeric types with associated unit metadata to preserve interpretability across systems. In practice, measurements may also include uncertainty estimates or measurement error, and unit normalization or conversion is often performed to enable meaningful comparisons across sources.

Applications of ratiodata span scientific, engineering, and economic domains. Examples include mass, length, time intervals, temperatures in Kelvin, income, file sizes, and other quantities with a true zero. Ratiodata enables meaningful aggregation and ratio-based feature engineering in analyses and machine learning, while also requiring careful handling of unit consistency and data quality.

See also: interval data, ordinal data, ratio data, units of measurement, data normalization.