Concept

Statistical assumption

Résumé
Statistics, like all mathematical disciplines, does not infer valid conclusions from nothing. Inferring interesting conclusions about real statistical populations almost always requires some background assumptions. Those assumptions must be made carefully, because incorrect assumptions can generate wildly inaccurate conclusions. Here are some examples of statistical assumptions: *Independence of observations from each other (this assumption is an especially common error). *Independence of observational error from potential confounding effects. *Exact or approximate normality of observations (or errors). *Linearity of graded responses to quantitative stimuli, e.g., in linear regression. Classes of assumptions There are two approaches to statistical inference: model-based inference and design-based inference. Both approaches rely on some statistical model to represent the data-generating process. In the model-based approach, the model is taken to be initially unknown, and on
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