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Statistical Power: Derivation, Development, and Data-Analytic Implications

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Abstract

In 1933, Neyman and Pearson introduced the interrelated concepts of statistical power and Type II error in response to a fundamental asymmetry in the hypothesis testing process. With few exceptions, however, statistical power did not become a regular textbook inclusion until some 30 years later. Modern concern for power evolved naturally from the “significance test controversy,” and was further stimulated by Cohen’s (1962) review in the Journal of Abnormal and Social Psychology. To date, eight power-analytic surveys have been conducted. Generally, the average power estimates derived from these analyses have been quite low. Providing sufficient power serves to decrease the commission of Type II errors, and may prevent misinterpretations of nonsignificant results. Including statistical power in the design and analysis of an experiment requires an a priori estimate of the effect size, as well as calculating obtained effect size. The obtained effect size reflects the relationship between the independent and dependent variables, and as such provides a better characterization of the research effort than does reporting only the significance level.

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Authors and Affiliations

  1. California State University, Sacramento, USA

    Lawrence J. Chase

  2. School of Speech Communication, Bowling Green State University, Bowling Green, Ohio, 43404, USA

    Raymond K. Tucker

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  1. Lawrence J. Chase
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Chase, L.J., Tucker, R.K. Statistical Power: Derivation, Development, and Data-Analytic Implications. Psychol Rec 26, 473–486 (1976). https://doi.org/10.1007/BF03394413

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