Distinguish internal validity from external validity.

Distinguish internal validity from external validity.

• A. Internal validity concerns generalizability, while external validity concerns causality within the study.
• B. Internal validity focuses on study design quality; external validity focuses on data collection methods.
• C. Internal validity deals with statistical significance; external validity deals with effect size.
• D. Internal validity is the degree to which observed effects can be attributed to the exposure studied within the study, while external validity is the generalizability of the findings to other populations or settings.

Answer: (D)

Internal validity is about whether the observed effects in the study can be truly attributed to the exposure or intervention being studied, without alternative explanations like confounding or bias within the study design. External validity asks whether those same effects would be seen in other populations, settings, or times beyond the study. This distinction is what the correct statement captures: it defines internal validity as the degree to which observed effects can be attributed to the exposure within the study, and external validity as the generalizability of the findings to other populations or settings. The other phrasings mix up these ideas. For example, swapping causality with generalizability, or tying internal validity to data collection methods, doesn’t reflect what internal and external validity actually measure. Likewise, tying internal validity to statistical significance and external validity to effect size confuses validity with standard statistical concepts.

Internal validity is about whether the observed effects in the study can be truly attributed to the exposure or intervention being studied, without alternative explanations like confounding or bias within the study design. External validity asks whether those same effects would be seen in other populations, settings, or times beyond the study. This distinction is what the correct statement captures: it defines internal validity as the degree to which observed effects can be attributed to the exposure within the study, and external validity as the generalizability of the findings to other populations or settings.

The other phrasings mix up these ideas. For example, swapping causality with generalizability, or tying internal validity to data collection methods, doesn’t reflect what internal and external validity actually measure. Likewise, tying internal validity to statistical significance and external validity to effect size confuses validity with standard statistical concepts.