This chapter examines the principles of human performance data analysis, the process in which investigators of error identify relationships between operator errors and their antecedents. Accident investigation methodology and the scientific method have similar objectives, to explain observed phenomena or events using formal methods of data collection and analysis. Determining the quality of data is critical because the quality of an investigation largely depends on the quality of the data that investigators collect. Investigative data should consistently match the sequence of occurrences and the period of time in which they occurred during the event. Before analyzing the collected data, investigators evaluate the data to assess their value to the investigation. In investigations of error, the predictor variables correspond to the antecedents and the outcome variable to the critical error. Investigators should search for multiple antecedents, even if one antecedent appears to adequately explain the error.