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The connection of generalization to specialization (or particularization) is reflected in the contrasting words hypernym and hyponym.A hypernym as a generic stands for a class or group of equally ranked items, such as the term tree which stands for equally ranked items such as peach and oak, and the term ship which stands for equally ranked items such as cruiser and steamer.
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.
A faulty generalization is an informal fallacy wherein a conclusion is drawn about all or many instances of a phenomenon on the basis of one or a few instances of that phenomenon. It is similar to a proof by example in mathematics . [ 1 ]
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In statistics, a population is a set of similar items or events which is of interest for some question or experiment. [1] [2] A statistical population can be a group of existing objects (e.g. the set of all stars within the Milky Way galaxy) or a hypothetical and potentially infinite group of objects conceived as a generalization from experience (e.g. the set of all possible hands in a game of ...
Faulty generalization fallacies can also affect any argument premise that uses a generalization. A problem with applying the statistical syllogism in real cases is the reference class problem : given that a particular case I is a member of very many reference classes F, in which the proportion of attribute G may differ widely, how should one ...
An anecdotal generalization is a type of inductive argument in which a conclusion about a population is inferred using a non-statistical sample. [8] In other words, the generalization is based on anecdotal evidence. For example: So far, this year his son's Little League team has won 6 of 10 games.
In statistics, a generalized linear model (GLM) is a flexible generalization of ordinary linear regression.The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value.