Uninformative

Uninformative is an adjective meaning that something does not provide useful or new information; it is not informative. The term can describe statements, data, questions, or priors in statistical reasoning.

In statistics, an uninformative prior (also called a noninformative prior) is a prior distribution intended to exert minimal influence on the posterior distribution, aiming to reflect ignorance about parameter values before observing data. Examples include a uniform prior on a bounded parameter or a Jeffreys prior in suitable models. However, true noninformativeness is context dependent, and priors can become informative under reparameterization or when data are limited. In practice, analysts often test several priors or perform sensitivity analysis to assess the impact on conclusions.

In information theory and communication, information content is tied to probability. Events or statements that are highly probable carry little information and can be described as uninformative in a given model. In reporting or analysis, uninformative content gives little new fact, context, or relevance, and may be revised or omitted to improve clarity and usefulness.

Usage notes: the term is generally neutral but can carry evaluative weight, depending on audience and context.