How Many Participants? How Many Trials? Maximizing the Power of Reaction Time Studies

Abstract

AbstractDue to limitations in the resources available for carrying out reaction time (RT) experiments, researchers often have to choose between testing relatively few participants with relatively many trials each or testing relatively many participants with relatively few trials each. To compare the experimental power that would be obtained under each of these options, I simulated virtual experiments using subsets of participants and trials from eight large real RT datasets examining 19 experimental effects. The simulations compared designs using the first $$N_T$$ N T trials from $$N_P$$ N P randomly selected participants, holding constant the total number of trials across all participants, $$N_P \! \times \! N_T$$ N P × N T . The $$[N_P,N_T]$$ [ N P , N T ] combination maximizing the power to detect each effect depended on how the mean and variability of that effect changed with practice. For most effects, power was greater in designs having many participants with few trials each rather than the reverse, suggesting that researchers should usually try to recruit large numbers of participants for short experimental sessions. In some cases, power for a fixed total number of trials across all participants was maximized by having as few as two trials per participant in each condition. Where researchers can make plausible predictions about how their effects will change over the course of a session, they can use those predictions to increase their experimental power.