Abstract
Statistical power is the ability to detect a significant effect, given that the effect actually exists in a population. Like most statistical concepts, statistical power tends to induce cognitive dissonance in hepatology researchers. However, planning for statistical power by an a priori sample size calculation is of paramount importance when designing a research study. There are five specific empirical components that make up an a priori sample size calculation: the scale of measurement of the outcome, the research design, the magnitude of the effect size, the variance of the effect size, and the sample size. A framework grounded in the phenomenon of isomorphism, or interdependencies amongst different constructs with similar forms, will be presented to understand the isomorphic effects of decisions made on each of the five aforementioned components of statistical power.
Highlights
Statistical power, like statistics in general, tends to induce feelings of cognitive dissonance in people [1]
The constructs of measurement, research design, magnitude and variance of effect size, and sample size are equal in their causal effects on the formation of statistical power in applied research
In order to bolster the understanding of this nebulous and cognitive dissonance-inducing construct in hepatology research, isomorphism can be applied as a framework to make better decisions when designing observational and experimental research
Summary
Statistical power, like statistics in general, tends to induce feelings of cognitive dissonance in people [1]. Statistical power is the ability to detect treatment effects, given that they truly exist in the population [2]. Statistical power is the chance that researchers will achieve a significant p value. Hepatology researchers must balance several different empirical factors that have causal and isomorphic effects on statistical power. Isomorphism is the phenomenon where constructs that are different in content, but similar in form, are linked due to their interdependent associations [3]. The constructs of measurement, research design, magnitude and variance of effect size, and sample size are all isomorphic in their effects on statistical power and each other. The constructs of measurement, research design, magnitude and variance of effect size, and sample size are equal in their causal effects on the formation of statistical power in applied research
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