Just because some parts are wrong, that doesn't mean that no parts are right. Models are expectation limiters, and experience predictors. Not all models are c

Just because some parts are wrong, that doesn't mean that no parts are right. Models are expectation limiters, and experience predictors. Not all models are created equal; they vary in specificity and in accuracy. The phrase "there are two cats on my bed", I'm pretty sure, is about as right as possible. This is my bed. I'm looking at two cats sleeping soundly on it. When I close my eyes, I can still feel them three. EVERY prediction that is limited by that model turns out to have been properly limited. EVERY prediction that model suggests is accurate. It is, in fact, as true as true gets. On the other hand, the sentence "light is a wave" is saying a much more specific thing about the universe. Like the two cats on my bed statement, it rules out some expectations, and suggests some predictions. As it turns out, some of those expectations are correctly limited, and some are not; some of the predictions are validated, and some are not. Therefor, we can say that, while it may be useful to consider light being a wave in some instances, it clearly is actually something other than a wave that just happens to be wave-like. If it was actually a wave, then every prediction would be validated, not just some of them. We can go a step further, even, and in some cases talk about the degree of truth of a given model. The truth value only has to be predictably higher than 0% in order to be useful; but it has to be 100% to be considered actually true. Furthermore, specificity effects the value of truth. A statement about cats on my bed may be 100% true, but it's not nearly as risky or specific as a statement about the fundamental nature of light. The main usefulness of this 100% true statement is for me to know that my feet will be kept warm. And, while I personally care deeply about the comfort of my feet, in the grand scheme of things, it's just not that big a deal.