nongeneralized

Nongeneralized is an adjective describing something that lacks generalization, that is, something not applicable beyond a specific case, context, or data set. In formal writing, it indicates conclusions, results, or theories that do not generalize to other situations or populations. The term is less common than non-generalizable or not generalizable; nongeneralized is more likely to appear in contexts where single-word modification is preferred or in translations.

In statistics and research, nongeneralized results have limited external validity; they reflect patterns observed only within the studied sample or setting. Researchers may note nongeneralized conclusions when sample bias, narrow scope, or measurement constraints prevent extrapolation.

In machine learning and data science, a nongeneralized model performs poorly on unseen data, indicating weak generalization from training to test data. Techniques such as cross-validation, regularization, and increasing data diversity are used to reduce nongeneralization.

In philosophy, linguistics, or law, nongeneralized claims or generalizations refer to statements that do not extend to all cases but apply to some subset or specific conditions.

Although meaningful, the term is often replaced by non-generalizable in technical writing to emphasize external validity or transferability. Nongeneralized serves as a concise descriptor of limits on applicability, serving to distinguish a context-limited perspective from universally applicable claims.