This paper briefly explores the roles of empiricism and determinism in science and engineering. From this analysis, I conclude that the intellectual exercise that we call “science” is best described as the transition from empiricism (i.e., from what we can observe) to determinism, which is the philosophy that the future can be predicted from the past on the basis of the natural laws that are condensations of all previous scientific knowledge. Thus, “science” is enacted by formulating theories to explain the observations and models, based on those theories, are developed to predict new phenomena. Accordingly, models are the computational arms of theories. Importantly, all models must possess a theoretical basis but not all theories need to predict. The structure of a deterministic model is that it must contain an input, a model “engine”, and an output, that are all linked by a feedback loop that permits the continual updating of the model parameters and a means of assessing predictions against new observations. This latter feature, in essence, enables the application of the “scientific method” of cyclical modification/assessment that continues until the model no longer accounts for new observations. At that point, the model (and possibly the theory, as well) must be discarded and a new theory/model developed. Again, importantly, no amount of successful prediction can “prove” a theory/model to be “correct”, because theories and models are merely the figments of our imagination as developed through imperfect senses and imperfect intellect. Accordingly, all theories and models are wrong at some level of detail. Contrariwise, a single failure of a model to predict an observation invalidates the theory/model unequivocally. The principal impediment to model building is complexity and it is the reason why a compromise must always be made between physical reality and mathematical tractability.