It is easy to have a theory of what is going on, to model the processes of how things are playing out inside the system, to make external predictions of the system, and to be utterly wrong.

Not because your model is wrong, but because either the boundary conditions were unexpected, or there was an anti-pattern in the data, or because the underlying assumptions of the model were violated by the data (in my case, this happened once when all the data was taken in the Southern Hemisphere...)

In all these cases, you can know what you're doing, you can know the boundaries of what what you're working with, and you can get results that surprise you. It's called "research" for a reason.

The model can also be ridiculously complex. Some of the equations I was dealing with took several lines to write down, and then only because I was substituting in other, complicated expressions to reduce the apparent complexity. It's easy to make mistakes - and so you can know what you're doing, and the boundaries that you're working with, and still have a mistake in the model that leads to a mistake in the data ... garbage in, garbage out.

In short, this shit is hard, yo!