Linked from

The 28 pages that link to External validity, each with the reason it gives.

• Causal inferenceRelated: A well-identified effect in one sample may not transfer to a different target population.

• Sampling biasRelated: A distorted sample weakens the case for generalizing findings to the target population.

• Field experimentRelated: Field settings can improve realism, but findings may still depend on place and population.

• Observational studyRelated: Real-world samples may reflect ordinary practice, but selection still limits generalization.

• Risk compensationRelated: A result from one safety setting may not predict behavior in another.

• Case studyRelated: It frames questions about how far conclusions from a bounded case can travel.

• Market researchRelated: A result from one sample or setting may not generalize to the wider market.

• Evidence-based policyRelated: A reliable result may still fail to transfer to the population or system a policy targets.

• RandomizationRelated: Treatment randomization strengthens internal comparisons but does not itself ensure generalizability.

• Trolley problemRelated: Laboratory choices about imaginary trolleys may not predict behavior in urgent real-world emergencies.

• Animal modelRelated: Animal results must generalize across species and experimental settings to inform human biology.

• Ecological validityNarrower topic: Ecological validity is often treated as one part of this broader generalizability question.

• Quasi-experimentRelated: A credible effect in one setting may not generalize to another.

• Scientific reproducibilityRelated: A result may reproduce under controlled conditions yet fail to generalize elsewhere.

• ExperimentRelated: Tightly controlled conditions may limit how far experimental results generalize.

• Nutritional epidemiologyRelated: Results from one cohort or trial may not generalize to different diets and populations.

• Field researchRelated: Real-world settings can strengthen relevance while limiting control over competing influences.

• Prediction intervalRelated: A wide interval warns that pooled findings may not transfer consistently across settings.

• Experimental economicsRelated: It frames the debate over carrying laboratory results into markets and policy.

• Statistical populationRelated: A result generalizes only to populations sufficiently like those supported by the study.

• Random assignment (experimental design)Compared with: Random assignment alone does not make a sample representative of other populations.

• Esther DufloRelated: A successful trial does not by itself show that a program will work elsewhere.

• In vitroRelated: It captures whether an in vitro finding generalizes to organisms or real-world settings.

• Economic methodologyRelated: It tests whether economic evidence from one context warrants broader policy conclusions.

• Laboratory mouseRelated: A result in laboratory mice may not generalize to humans or other settings.

• Abhijit BanerjeeRelated: Local trial results do not automatically predict how programs will work elsewhere.

• Michael KremerRelated: Development experiments raise the question of whether local results transfer to other communities.

• Representative sampleRelated: Representativeness supports generalization, but does not alone establish external validity.