Empirical models are the subject of this chapter. We begin by defining what an empirical model is and its relationship to the data. We discuss how to understand an empirical model under the model-based account. We argue that empirical models can be useful in one or more of three ways: prediction, measurement, and characterization. We pay particular attention to theory testing as the most common use of empirical models and the use to which empirical models are least suited. We demonstrate that the combination of a hypothetico-deductive relationship between the theoretical model and the hypothesis to be tested and a hypothetico-deductive relationship between the hypothesis to be tested and the data prevents empirical model testing. This logic holds regardless of the statistical approach—falsificationist, verificationist, or Bayesian—taken. We then address the other uses of empirical modeling by presenting examples of empirical models drawn from the literature that eschew theory testing while remaining useful, and by most accounts, scientific.
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