According to the text reliability refers to the stability of a test over repeated administration . A reliable test will yield stable scores over repeated administrations assuming the trait being measured has not change ( Kubiszyn & Borich 2010).

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In statistics, reliability refers to the consistency of a measure. A measure is said to have a high reliability if it produces consistent results under consistent conditions. For example, measurements of people’s height and weight are often extremely reliable. There are several general classes of reliability estimates: Reliability does not imply validity. That is, a reliable measure that is measuring something consistently, may not be measuring what you want to be measuring. For example, while there are many reliable tests of specific abilities, not all of them would be valid for predicting, say, job performance. In terms of accuracy and precision, reliability is analogous to precision, while validity is analogous to accuracy. While reliability does not imply validity, a lack of reliability does place a limit on a tests overall validity. A test that is not perfectly reliable cannot be perfectly valid, either as a means of measuring attributes of a person or as a means of predicting ( mindmeister)

Validity and Reliability