How do we go about weighing evidence, testing hypotheses, and making inferences? According to this work, we infer the hypothesis that would, if correct, provide the best explanation of the available evidence. Articulating the model of "Inference to the Best Explanation" requires an account of what makes one explanation better than another. Lipton distinguishes between the explanation best supported by the evidence - the likeliest explanation - and the explanation that would, if true, provide the most understanding - the loveliest explanation. He argues that an illuminating version of "Inference to the Best Explanation" must rely on the latter notion, and provides a new account of what makes one explanation lovelier than another. He does this by analyzing the structure of contrastive explanations, explanations that answer the form "why P rather than Q?". The analysis of contrastive explanation is then shown to support a strong version of "Inference to the Best Explanation" that reveals how explanatory considerations can be a guide to inference.

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