How many trials does it take to get a significant ERP effect? It depends

Area of Science:

• Cognitive Neuroscience
• Psychophysiology
• Neuroscience

Background:

• Event-related potential (ERP) studies require balancing statistical power with recording session length.
• Previous research quantified minimum trials for reliable ERP components but often overlooked sample size and effect magnitude.
• Statistical power in ERP research is influenced by trial count, participant numbers, and effect size.

Purpose of the Study:

• To investigate the interaction between trial count, participant number, and effect magnitude on statistical power in ERP studies.
• To provide improved guidance for selecting the optimal number of trials in ERP research design.
• To assess how these factors influence the detection of ERP effects and group differences.

Main Methods:

• Employed a Monte Carlo simulation approach to estimate statistical power.
• Examined the probability of significant results for ERP effects, within-participant differences, and between-participant group differences.
• Focused on the error-related negativity (ERN) and lateralized readiness potential (LRP) components.

Main Results:

• Doubling the recommended number of trials often more than doubled statistical power.
• Statistical power is significantly influenced by the interplay of trial number, sample size, and effect magnitude.
• General recommendations for trial numbers may be insufficient without considering other key variables.

Conclusions:

• Researchers must integrate sample size, anticipated effect magnitude, and noise levels when determining ERP trial counts.
• Sole reliance on recommendations for achieving a "stable" ERP waveform is inadequate for optimizing study power.
• A nuanced approach considering multiple interacting factors is crucial for robust ERP study design.