Abstract

Suppose that several observations “coincide,” meaning that they are similar in some interesting respect. Is this coinciding a mere coincidence, or does it derive from a common cause? Those who reason about this kind of question—whether they embrace the first answer or the second—often deploy a mode of inference that I call probabilistic modus tollens. In this chapter I criticize probabilistic modus tollens and consider likelihood and Bayesian frameworks for reasoning about coincidences. I also consider the perspective offered by model selection theory (including the Akaike information criterion), and argue that model selection often provides important insights.