Optimizing Research Payoff

In this article, we present a model for determining how total research payoff depends on researchers' choices of sample sizes, α levels, and other parameters of the research process. The model can be used to quantify various trade-offs inherent in the research process and thus to balance competing goals, such as (a) maximizing both the number of studies carried out and also the statistical power of each study, (b) minimizing the rates of both false positive and false negative findings, and (c) maximizing both replicability and research efficiency. Given certain necessary information about a research area, the model can be used to determine the optimal values of sample size, statistical power, rate of false positives, rate of false negatives, and replicability, such that overall research payoff is maximized. More specifically, the model shows how the optimal values of these quantities depend upon the size and frequency of true effects within the area, as well as the individual payoffs associated with particular study outcomes. The model is particularly relevant within current discussions of how to optimize the productivity of scientific research, because it shows which aspects of a research area must be considered and how these aspects combine to determine total research payoff.