This paper shows how to recover behavioral biases from revealed preference rankings implied by choices. The approach is sufficiently flexible to account for many well-documented behavioral biases in the literature. I apply the model to workhorse datasets in the literature on choice under risk and scanner consumer choices. I show that accounting for simple behavioral biases such as inattention and salience thinking suffices to explain most violations of rationality. Finally, I demonstrate that well-known regularities, such as the correlates of risk aversion, might in fact be confounded by simple behavioral biases. Overall, this research delineates a robust method for extracting behavioral biases from choice data, thereby not only enhancing our understanding of consumer decision-making but also offering a fresh lens through which to explore the contours of rationality in human behavior.