We can record brain activity (the electroencephalogram or EEG) from electrodes on the scalp, even though the skull and scalp separate the electrodes from the brain. However, the signals we record this way are often contaminated by noise and artifacts that are an order of magnitude larger than the EEG signals of interest. It is therefore essential to pay close attention to the quality of the data in order to obtain stable, meaningful, and statistically significant effects. Until recently, however, there was no widely accepted method for quantifying data quality for EEG signals, especially for the event-related potentials (ERPs) embedded within the EEG. In this chapter, I describe a new metric of ERP data quality, called the standardized measurement error (SME). I provide an intuitive description of how it works and then I provide examples of how it has been used to quantify data quality across several common ERP paradigms and to determine which EEG and ERP processing operations do the best job of maximizing data quality. I also describe how the SME can be extended to time-frequency analyses and how psychometric measures of reliability can be used to assess data quality in studies that focus on individual differences.