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Better Accuracy for Better Science . . . Through Random Conclusions
Clintin P Davis-Stober1, Jason Dana2, David Kellen3
1Department of Psychological Sciences, MU Institute for Data Science and Informatics, University of Missouri.
None:
Conducting research with human subjects can be difficult because of limited sample sizes and small empirical effects. We demonstrate that this problem can yield patterns of results that are practically indistinguishable from flipping a coin to determine the direction of treatment effects. We use this idea of random conclusions to establish a baseline for interpreting effect-size estimates, in turn producing more stringent thresholds for hypothesis testing and for statistical-power calculations. An examination of recent meta-analyses in psychology, neuroscience, and medicine confirms that, even if all considered effects are real, results involving small effects are indeed indistinguishable from random conclusions.
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