Lachlan Hall, Amy Dawel, Lisa‐Marie Greenwood, Conal Monaghan, Kevin Berryman, Bradley N. Jack

Estimating statistical power for ERP studies using the auditory N1, Tb, and P2 components

  • Experimental and Cognitive Psychology
  • Neuropsychology and Physiological Psychology
  • Biological Psychiatry
  • Cognitive Neuroscience
  • Developmental Neuroscience
  • Endocrine and Autonomic Systems
  • Neurology
  • Experimental and Cognitive Psychology
  • Neuropsychology and Physiological Psychology
  • General Neuroscience
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AbstractThe N1, Tb, and P2 components of the event‐related potential (ERP) are thought to reflect the sequential processing of auditory stimuli in the human brain. Despite their extensive use in biological, cognitive, and clinical neuroscience, there are no guidelines for how to appropriately power ERP studies using these components. In the present study, we investigated how the number of trials, number of participants, effect magnitude, and study design influenced statistical power. Using Monte Carlo simulations of ERP data from a passive listening task, we determined the probability of finding a statistically significant effect in 58,900 experiments repeated 1,000 times each. We found that as the number of trials, number of participants, and effect magnitude increased, so did statistical power. We also found that increasing the number of trials had a bigger effect on statistical power for within‐subject designs than for between‐subject designs, and that within‐subject designs required a smaller number of trials and participants to provide the same level of statistical power for a given effect magnitude than between‐subject designs. These results show that it is important to carefully consider these factors when designing ERP studies, rather than relying on tradition or anecdotal evidence. To improve the robustness and reproducibility of ERP research, we have built an online statistical power calculator (https://bradleynjack.shinyapps.io/ErpPowerCalculator), which we hope will allow researchers to estimate the statistical power of previous studies, as well as help them design appropriately‐powered studies in the future.

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