![]() The statistical concepts of sampling theory and hypothesis testing have become intermingled with the notion of generalizability. In other words, generalization is the “big picture” interpretation of a study's results once they are determined to be internally valid. ![]() After all, we generalize results from animal studies to humans, if the common biologic process or disease mechanism is “relevant” and species is relatively “irrelevant.” We also draw broad inferences from randomized controlled trials, even though these studies often have specific inclusion and exclusion criteria, rather than being population probability samples. ![]() The generalizability of a study's results depends on the researcher's ability to separate the “relevant” from the “irrelevant” facts of the study, and then carry forward a judgment about the relevant facts, 2 which would be easy if we always knew what might eventually turn out to be relevant. Whether or not those internally valid results will then broadly “generalize,” to other study settings, samples, or populations, is as much a matter of judgment as of statistical inference. Thoughtful study design, careful data collection, and appropriate statistical analysis are at the core of any study's internal validity. First, are the results of the study true, or are they an artifact of the way the study was designed or conducted i.e., is the study is internally valid? Second, are the study results likely to apply, generally or specifically, in other study settings or samples i.e., are the study results externally valid? Confusion around generalizability has arisen from the conflation of 2 fundamental questions.
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