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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 acknowledges and allows for variability in assessment conditions that may affect measurements. The advantage of G theory lies in the fact that researchers can estimate what proportion of the total variance in the results is due to the individual factors that often vary in assessment, such as setting, time, items, and raters.
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.
The basic starting point for almost all theories of test reliability is the idea that test scores reflect the influence of two sorts of factors: [7] Consistency factors: stable characteristics of the individual or the attribute that one is trying to measure.
This is known as the bias–variance tradeoff. Keeping a function simple to avoid overfitting may introduce a bias in the resulting predictions, while allowing it to be more complex leads to overfitting and a higher variance in the predictions.
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 ...
In the model-based approach, the model is taken to be initially unknown, and one of the goals is to select an appropriate model for inference. In the design-based approach, the model is taken to be known, and one of the goals is to ensure that the sample data are selected randomly enough for inference.