Generalisability

Generalisability, also called external validity, is the extent to which a study's findings can be applied beyond the specific sample, setting, or conditions in which the research was conducted. It concerns whether observed effects and relationships hold in other populations, times, places, or contexts.

In quantitative research, generalisability emphasizes external validity and population inference. In qualitative research, the term transferability is often used; readers judge applicability to other contexts based on thick description of methods, participants, and setting.

Threats to generalisability include nonrepresentative samples and selection bias, measurement bias, and artificial experimental conditions. Temporal changes, cultural differences, and context-specific mechanisms can also limit applicability. The extent of generalisability depends on sampling design, similarity of contexts, and whether mechanisms are universal or context dependent.

Strategies to improve generalisability include probability sampling, larger and more diverse samples, replication across sites, and meta-analysis. Transparent reporting of population characteristics and context helps readers judge applicability. In qualitative work, thick description and theoretical sampling support analytic generalisation rather than broad statistical generalisation.

Generalisation is essential for evidence-based practice, policy evaluation, and theory development, guiding decisions about when findings from one study can inform others.