Generaliserbart

Generaliserbart is a Swedish adjective meaning capable of being generalized. In scientific usage, it refers to external validity—the extent to which results hold beyond the specific sample or setting in which they were observed. A result is described as generaliserbart when the underlying processes, relationships, or effects are expected to apply to a broader population or range of contexts, not only to the studied case.

In research methods, generaliserbart depends on the representativeness of the sample, study design, and the similarity between study conditions and target conditions. Threats to generaliserbarhet include selection bias, measurement bias, and contextual differences. Researchers assess external validity by considering demographic diversity, real-world conditions, and domain constraints; replication and meta-analysis further support generaliserbarhet.

In statistics and social science, generaliserbart results inform decisions about applicability of conclusions to policy, practice, or theory. In machine learning, the analogous concept is model generalization: a model is generaliserbart when it performs well on unseen data. Overfitting reduces generaliserbarhet; techniques such as cross-validation, regularization, and diverse training data aim to improve it. Domain adaptation and transfer learning address generalization across different settings.

Generaliserbart also appears in philosophy of science to describe the scope of general laws or theories and their boundary conditions. The term emphasizes that applicability is not guaranteed and depends on context, assumptions, and the similarity between studied and target scenarios.

See also external validity, replication, transfer learning.