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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.
Generalization is the concept that humans and other animals use past learning in present situations of learning if the conditions in the situations are regarded as similar. The learner uses generalized patterns, principles, and other similarities between past experiences and novel experiences to more efficiently navigate the world. 
The universal law of generalization is a theory of cognition stating that the probability of a response to one stimulus being generalized to another is a function of the “distance” between the two stimuli in a psychological space.
A generalization is a form of abstraction whereby common properties of specific instances are formulated as general concepts or claims.  Generalizations posit the existence of a domain or set of elements, as well as one or more common characteristics shared by those elements (thus creating a conceptual model ).
Generalization in psychological terms is the measure of how a theory holds up when applied in a non-experimental environment. Hence, generalised game theory takes elements from this quality and applies them to game theories. Many traditional Nash equilibriums can be applied to social and psychological interactions through generalization.
One of the simplest settings for generalizations is to vector valued functions of several variables (most often the domain forms a vector space as well). This is the field of multivariable calculus . In one-variable calculus, we say that a function is differentiable at a point x if the limit exists. Its value is then the derivative ƒ' ( x ).
For supervised learning applications in machine learning and statistical learning theory, generalization error  (also known as the out-of-sample error  or the risk) is a measure of how accurately an algorithm is able to predict outcome values for previously unseen data.