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Abstract

Reliability in classical test theory is a population-dependent concept, defined as a ratio of true-score variance and observed-score variance, where observed-score variance is a sum of true and error components. On the other hand, the power of a statistical significance test is a function of the total variance, irrespective of its decomposition into true and error components. For that reason, the reliability of a dependent variable is a function of the ratio of true-score variance and observed-score variance, whereas statistical power is a function of the sum of the same two variances. Controversies about how reliability is related to statistical power often can be explained by authors’ use of the term “reliability” in a general way to mean “consistency,” “precision,” or “dependability,” which does not always correspond to its mathematical definition as a variance ratio. The present note shows how adherence to the mathematical definition can help resolve the issue and presents some derivations and illustrative examples that have further implications for significance testing and practical research.

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