  Asymmetry is an inherent property of bivariate associations and therefore
must not be ignored. The currently applicable dependence measures mask the
potential asymmetry of the underlying dependence structure by implicitly
assuming that quantity Y is equally dependent on quantity X, and vice versa,
which is generally not true. We introduce the copula-based dependence measure
qad that quantifies asymmetry. Specifically, qad is applicable in general
situations, is sensitive to noise in data, detects asymmetry in dependence and
reliably quantifies the information gain/predictability of quantity Y given
knowledge of quantity X, and vice versa. Using real-world data sets, we
demonstrate the relevance of asymmetry in associations. Asymmetry in dependence
is a novel category of information that provides substantial information gain
in analyses of bivariate associations.
