You're Wrong

So am I. It's ok. We all operate with a model of our/the world, constructed in our mind. It is impossible for any model to be 100% accurate. If it were, it would have to BE the world. The only thing which can accurately reproduce all the phenomena in the universe, is all the universe itself. So here is a trade-off: We want something to help us predict what actions we should take, for our future benefit (guide our behavior). But it has to fit in our mind (or computers), and not use too much of our mind's energy or time. We need to evaluate a model based on it's usefulness. How useful is it in helping us predict what will happen, so we can prepare in advance? In our mind, for example, we likely have a model of the movement of massive objects. If we see a truck on a path to cross our current path, we can predict what will happen, and change our trajectory BEFORE this potential future event (fatal collision). A model's accuracy or correctness is certainly necessary for it's usefulness. But it would not be that useful if one required the correctness/accuracy to be at/near 100%, at the expense of too much time or energy. If it took more time to run your calculation through the model, than it took for the truck to hit you, the model would have been not useful to you. Some models are good enough to do some things. But just because, for example, a belief in a deity can provide a unifying focus, sometimes useful for organizing groups to act together for causes, or maybe provide some comfort from tragic events, it doesn't mean that model (e.g. a deity controls the universe) is accurate (true). In situations where one does have relatively more time, it is often well worth it to use a more complex/accurate model, based on verifiable data, and proven to be able to enable one to steer away from common pitfalls, towards desired outcomes. Science is not a comprehensive model. It is a means to test models. It is hard to argue against science, because it has a small job, and it does it well. It is simply the trial and error testing used to, bit by bit, test parts of models. It simply tells you if your assumptions can't be useful in predicting what to do next, if they just don't add up, and/or are internally inconsistent. The scientific method, itself, will not be able to tell you what to do. It is just a tool to test whether or not your model is likely to be useful in telling you what to do.