distributionwise

Distributionwise is a term used in statistics and data analysis to describe methods or perspectives that emphasize the properties of probability distributions rather than individual observations. The phrase does not point to a single standardized methodology; instead, it signals an approach that analyzes data through distributional characteristics such as shape, spread, and tails.

In statistical practice, distributionwise thinking underpins methods that model or compare entire distributions. This includes distributional regression, which seeks to model the full conditional distribution of a response variable rather than only its mean, as well as quantile regression, which targets specific points in the distribution. Distribution-wise analysis also relates to distribution-free or nonparametric methods that rely on distributional invariants and to studies of distributional shift, where the distribution of data changes across time, groups, or settings.

In broader usage, the label may appear in discussions, software documentation, or workflows to denote analyses that summarize or compare data by distributional properties such as moments, skewness, and kurtosis, or by the actual probability distributions involved. This contrasts with approaches focused on single statistics or itemwise summaries.

Notes on usage and scope: the exact meaning of distributionwise can vary by discipline and context. When encountered, it is helpful to examine how the term is defined within that specific analytical framework. See also distribution, probability distribution, distributional statistics, distributional regression, quantile regression, and distributional shift.