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||The degree to which a model is transparent. A model is said to be transparent if all key assumptions that underlie the model are accessible and understandable for the users.|
|Type I error
||also: Error of the first kind. In hypothesis testing,
this error is caused by incorrect
rejection of the hypothesis when it is true.
Any test is at risk of being too selective and too sensitive.
The design of the test, especially confidence limits, aims
at reducing the likelihood of one type of error at the
price of increasing the other. Thus, all such statistical
tests are value laden.|
|Type II error
||also: Error of the second kind. In hypothesis testing this error is caused by not rejecting the
hypothesis when it is false.
|Type III error
||also: Error of the third kind. Assessing or solving the wrong problem by incorrectly accepting the false
meta-hypothesis that there is no difference between the boundaries of a problem, as defined by
the analyst, and the actual boundaries of that problem (Raifa, 1968, redefined by Dunn, 1997,
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