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Test validity is the extent to which a test (such as a chemical, physical, or scholastic test) accurately measures what it is supposed to measure.In the fields of psychological testing and educational testing, "validity refers to the degree to which evidence and theory support the interpretations of test scores entailed by proposed uses of tests". [1]
A test cannot be valid unless it is reliable. Validity is also dependent on the measurement measuring what it was designed to measure, and not something else instead. [ 6 ] Validity (similar to reliability) is a relative concept; validity is not an all-or-nothing idea.
For example, while there are many reliable tests of specific abilities, not all of them would be valid for predicting, say, job performance. While reliability does not imply validity, reliability does place a limit on the overall validity of a test. A test that is not perfectly reliable cannot be perfectly valid, either as a means of measuring ...
Reliability is supposed to say something about the general quality of the test scores in question. The general idea is that, the higher reliability is, the better. Classical test theory does not say how high reliability is supposed to be. Too high a value for , say over .9, indicates redundancy of items.
In statistics, intra-rater reliability is the degree of agreement among repeated administrations of a diagnostic test performed by a single rater. [ 1 ] [ 2 ] Intra-rater reliability and inter-rater reliability are aspects of test validity .
[3] Criterion validity is typically assessed by comparison with a gold standard test. [ 4 ] An example of concurrent validity is a comparison of the scores of the CLEP College Algebra exam with course grades in college algebra to determine the degree to which scores on the CLEP are related to performance in a college algebra class. [ 5 ]
In medicine and medical statistics, the gold standard, criterion standard, [1] or reference standard [2] is the diagnostic test or benchmark that is the best available under reasonable conditions. [3] It is the test against which new tests are compared to gauge their validity, and it is used to evaluate the efficacy of treatments. [1]
A development in medical statistics is the use of out-of-sample cross validation techniques in meta-analysis. It forms the basis of the validation statistic, Vn, which is used to test the statistical validity of meta-analysis summary estimates.