Beyond Average: Modeling and Characterizing Variability to Enable Valid and Accurate Predictions

Recent failed attempts to replicate numerous findings in psychology have raised concerns about common methodological practices. More caution appears to be required when summarizing and modeling data, to enable accurate out-of-sample predictions. Here, I argue that signal variability, although commonly discarded in traditional analyses, often includes meaningful and essential information.