I review the classic literature in generative grammar and Marr's three-level program for cognitive science to defend the Evaluation Metric as a psychological theory of language learning. Focusing on well-established facts of language variation, change, and use, I argue that optimal statistical principles embodied in Bayesian inference models are ill-suited for language acquisition. Specific attention will be given to the Subset Problem: Indirect negative evidence, which can be attractively formulated in the Bayesian framework, is ineffective when the statistical properties of language are examined in detail. As an alternative, I suggest that the Tolerance Principle (Yang 2016) provides a unified solution for the problem of induction and generalization: It bridges the computational and algorithm levels in Marr's formulation, while retaining the commitment to the formal and empirical constraints in child language development.