Abstract
In designing an ERP study, researchers must choose how many trials to include, balancing the desire to maximize statistical power and the need to minimize the length of the recording session. Recent studies have attempted to quantify the minimum number of trials needed to obtain reliable measures for a variety of ERP components. However, these studies have largely ignored other variables that affect statistical power in ERP studies, including sample size and effect magnitude. The goal of the present study was to determine whether and how the number of trials, number of participants, and effect magnitude interact to influence statistical power, thus providing a better guide for selecting an appropriate number of trials. We used a Monte Carlo approach to measure the probability of obtaining a statistically significant result when testing for (a) the presence of an ERP effect, (b) within-participant condition differences in an ERP effect, and (c) between-participants group differences in an ERP effect. Each of these issues was examined in the context of the error-related negativity and the lateralized readiness potential. We found that doubling the number of trials recommended by previous studies led to more than a doubling of statistical power under many conditions. Thus, when determining the number of trials that should be included in a given study, researchers must consider the sample size, the anticipated effect magnitude, and the noise level, rather than relying solely on general recommendations about the number of trials needed to obtain a "stable" ERP waveform.
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