Why is covariance less useful than correlation for comparing the strength of relationships?

Covariance is often considered less useful than correlation for comparing the strength of relationships because its magnitude is dependent on the units of measurement used for the variables.1

Because covariance is expressed in the product of the variables' units, its value can be difficult to interpret; for instance, measuring age in years and height in centimeters results in units of "centimeter-years," which are uninformative. Furthermore, covariance has no upper or lower bound, making it impossible to determine the relative strength of a relationship based on its magnitude alone.1

In contrast, correlation is a normalized version of covariance that is independent of the variables' units. The correlation coefficient is constrained to a range between -1 and 1, where the magnitude directly indicates the strength of the association. This standardization allows for a clear, consistent comparison of relationships regardless of the underlying data scales.12