"One-reason decision making" is a label for a class of fast and frugal heuristics that base decisions on only one reason. These heuristics do not attempt to optimally fit parameters to a given environment; rather, they have simple structural features and "bet" that the environment will fit them. By not attempting to optimize, these heuristics can save time and computations, and demand only little knowledge concerning a situation. Models of one-reason decision making have been designed for various tasks, including choice, numerical estimation, and classification. This chapter focuses on two of these heuristics, "Take The Best" and Minimalist, and compare their performance with that of standard statistical strategies that weigh and combine many reasons, such as multiple regression. Contrary to common intuition, more reasons are not always better.