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In other words, it is the extent to which the results of a study can generalize or transport to other situations, people, stimuli, and times. [2] [3] Generalizability refers to the applicability of a predefined sample to a broader population while transportability refers to the applicability of one sample to another target population. [2]
Generalizability theory, or G theory, is a statistical framework for conceptualizing, investigating, and designing reliable observations. It is used to determine the reliability (i.e., reproducibility) of measurements under specific conditions. It is particularly useful for assessing the reliability of performance assessments.
In other words, the relevance of external and internal validity to a research study depends on the goals of the study. Furthermore, conflating research goals with validity concerns can lead to the mutual-internal-validity problem, where theories are able to explain only phenomena in artificial laboratory settings but not the real world.
In statistics, it may involve basing broad conclusions regarding a statistical survey from a small sample group that fails to sufficiently represent an entire population. [ 1 ] [ 6 ] [ 7 ] Its opposite fallacy is called slothful induction , which consists of denying a reasonable conclusion of an inductive argument (e.g. "it was just a ...
Also confidence coefficient. A number indicating the probability that the confidence interval (range) captures the true population mean. For example, a confidence interval with a 95% confidence level has a 95% chance of capturing the population mean. Technically, this means that, if the experiment were repeated many times, 95% of the CIs computed at this level would contain the true population ...
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Key to the game. This is a fascinating matchup between two of the most creative offensive minds in college football. Dillingham has schemed an offense that has scored 53 TDs this season while ...
Replication in statistics evaluates the consistency of experiment results across different trials to ensure external validity, while repetition measures precision and internal consistency within the same or similar experiments. [5] Replicates Example: Testing a new drug's effect on blood pressure in separate groups on different days.