Generaliserbara

Generaliserbara is a Swedish adjective meaning capable of being generalized beyond the original data, sample, or context. In English, it is commonly translated as generalizable. The term is used to describe findings, conclusions, or models that can reasonably apply to populations, settings, or times beyond those directly studied. It is a central concept in discussions of external validity and transferability.

Key considerations for generaliserbarhet include how representative the sample is of the target population, the variability of the studied conditions, and the extent to which the measurement tools accurately capture the intended constructs. Studies aiming for generaliserbara results typically address issues such as sampling bias, measurement reliability, and the explicit documentation of context. Researchers may test generalisability by replication across different populations, settings, or time periods, or by conducting cross-context analyses.

The concept is closely related to external validity in quantitative research and to transferability in qualitative work. In machine learning, an analogous idea is generalization: a model should perform well on unseen data rather than only on the training set, with overfitting reducing generaliserbarhet.

Limitations are often acknowledged: findings may be generaliserbara only within specified contexts or under certain assumptions. Clear reporting of the study scope, context, and any restrictions on applicability enhances the usefulness of generaliserbara conclusions.

See also: external validity, transferability, generalization, overfitting, cross-validation, sampling bias.