noninferable

Noninferable is an adjective describing something that cannot be successfully inferred from known facts, evidence, or premises. It denotes that there is no reliable inferential link from the information available to the conclusion, either because the evidence is insufficient, ambiguous, or logically insufficient to support the inference.

In philosophy and logic, a proposition is noninferable from a given set of premises if there is no valid argument that derives it from those premises. In statistics and data analysis, a parameter or outcome may be noninferable when the data do not provide enough information to identify or estimate it uniquely, a situation known as non-identifiability or underdetermination.

A simple example is a dataset that cannot distinguish between two competing models that predict the same outcomes; from the data alone, one cannot infer which model is correct. In a broader sense, noninferability can arise from limited or noisy data, ambiguous definitions, or structural features of a system that prevent unique conclusions.

Related concepts include nonfalsifiability, nonidentifiability, and indeterminacy. Noninferability focuses on the limits of what can be inferred rather than on the truth value of a claim, highlighting where additional evidence, different methods, or explicit uncertainty is required.

Practically, recognizing noninferability informs methodological choices: it may prompt collecting more data, refining the model, or adopting probabilistic or qualitative approaches that acknowledge uncertainty rather than asserting definitive conclusions.