Generalizability

Generalizability, also known as external validity, refers to the extent to which the results of a study can be applied beyond the specific sample, setting, or conditions in which the study was conducted. It concerns whether findings hold for other people, places, times, or tasks. In research design, high generalizability increases the usefulness of results for theory, policy, or practice, but it often competes with internal validity, which focuses on causal conclusions within the study.

Several factors influence generalizability. The degree of representativeness of the sample and the similarity of the study context to real-world settings are important. Measurement equivalence, consistency of procedures, and the heterogeneity of populations and settings also affect transportability. Threats include selection bias, nonresponse, cultural or contextual differences, and temporal changes that limit applicability across times or places.

Researchers seek to enhance generalizability through strategies such as diverse, multi-site sampling, larger and more representative samples, replication across contexts, and transparent reporting of limitations. Meta-analyses synthesize evidence across studies to estimate average effects and explore variation in effects across populations. In causal inference, transportability frameworks address how to adapt findings to new populations. Generalizability is inherently context-dependent. What generalizes in one domain or culture may not in another, and methodological trade-offs with internal validity can constrain external applicability. Recognizing these limits helps researchers and practitioners assess the relevance of evidence for different populations and settings.