Baseline correction plays an important role in past and current methodological debates in ERP research (e.g., the Tanner vs. Maess debate in the Journal of Neuroscience Methods), serving as a potential alternative to strong high‐pass filtering. However, the very assumptions that underlie traditional baseline also undermine it, implying a reduction in the signal‐to‐noise ratio. In other words, traditional baseline correction is statistically unnecessary and even undesirable. Including the baseline interval as a predictor in a GLM‐based statistical approach allows the data to determine how much baseline correction is needed, including both full traditional and no baseline correction as special cases. This reduces the amount of variance in the residual error term and thus has the potential to increase statistical power.