In designing a research program, it is useful to distinguish between uncanny and mundane predictions. An uncanny prediction is one that is incongruous with both current scientific understanding of the phenomenon and any corresponding folk models, i.e., the prediction specifies aspects of the world that are hitherto unrecognized. In contrast, a mundane prediction is one that is consistent with our current understanding of the world, be it scientific or folk. If supported, uncanny predictions have a large impact on scientific knowledge – they provide substantial prima facie evidence supporting the hypothesis from which they were derived, and they open up new areas of empirical exploration. In contrast, when mundane predictions are supported, they have far less impact on scientific knowledge. Typically, a variety of existing perspectives can account for familiar phenomena, hence supported mundane predictions provide marginal evidence for the hypothesis from which they were derived; likewise, because the given effects are already familiar, such findings do not lead to new areas of empirical exploration. Most uncanny predictions will fail, and most mundane predictions will succeed. This is because existing scientific perspectives, and many folk models, will generally be accurate, hence hypotheses that are incongruent with such knowledge will often be incorrect, while hypotheses that are congruent with it will often be correct. Phrased in cost/benefit terms, uncanny predictions are thus a high-risk, high-yield enterprise, while mundane predictions are a low-risk, low-yield enterprise.