Enhancing Statistical Power While Maintaining Small Sample Sizes in Behavioral Neuroscience Experiments Evaluating Success Rates

Abstract

Studies with low statistical power reduce the probability of detecting true effects and often lead to overestimated effect sizes, undermining the reproducibility of scientific results. While several free statistical software tools are available for calculating statistical power, they often do not account for the specialized aspects of experimental designs in behavioral studies that evaluate success rates. To address this gap, we developed "SuccessRatePower" a free and user-friendly power calculator based on Monte Carlo simulations that takes into account the particular parameters of these experimental designs. Using "SuccessRatePower", we demonstrated that statistical power can be increased by modifying the experimental protocol in three ways: 1) reducing the probability of succeeding by chance (chance level), 2) increasing the number of trials used to calculate subject success rates, and, in some circumstance, 3) employing statistical analyses suited for discrete values. These adjustments enable even studies with small sample sizes to achieve high statistical power. Finally, we performed an associative behavioral task in mice, confirming the simulated statistical advantages of reducing chance levels and increasing the number of trials in such studies