Over the last decade, many EEG/ERP studies have suffered from low statistical power and bad reproducibility. Still, the adaption to alternative approaches progresses only slowly, and in most cases, researchers stick to established methods. Linear mixed effects models are a promising alternative to the established two-stage approach in the ERP analysis. However, a comparison between the two-stage approach and the linear mixed models approach in the ERP analysis concerning the number of subjects and items and the between-subject variability has not been conducted yet. In this thesis, we introduce the toolbox UnfoldSim.jl for simulating EEG data with subject and item effects. Furthermore, based on simulated ground truth data, we investigate the statistical power of the two-stage approach and the linear mixed model approach regarding the number of subjects and items as well as the between-subject variability. We observed, as expected, a gain in statistical power by increasing the number of subjects and items for both modelling schemes. In contrast to our expectation, the conducted power analysis showed no significant advantage of the linear mixed model approach against the two-stage approach concerning the number of subjects and items and varying between-subject variances. Additionally, we observed an increased type I error rate for the linear mixed model approach for simulated data with a varying subject slope. However, the results should be considered with caution. The analysis is based on simulated EEG data, and the applicability to real ERP analyses needs further investigation. The toolbox UnfoldSim.jl is a good starting point to explore the applicability to real EEG data in more detail. Furthermore, based on more realistic simulations, the UnfoldSim.jl toolbox, combined with a subsequent power analysis, could be a possibility to determine the needed number of subjects and items to reach sufficient power for future studies.