Part of the difficulty with the discussion of whether or not randomness exists is the problem of defining just what, exactly, randomness is. It's not as simple as it looks. Let's say you have a binary sequence. Perhaps you'd like to define this sequence as random if there does not exist a mathematical formula for determining the next bit in the sequence given the previous bits, but every sequence encountered in real life is always finite. And given a finite sequence, it is no problem to construct a formula that describes every bit in the sequence. Just make your formula sufficiently large, and presto! And if you think you have a formula that works, and it gets the next bit wrong, that doesn't necessarily prove the sequence is random either. There may always be some other formula that would have gotten it right.
The probabilistic model is simply the last mathematical model that we have to resort to when we can find no better model (or modeling it deterministically would be infeasible) and observations lead us to have a reasonable degree of confidence that the distribution is uniform. But even the randomness tests themselves are subject to question. Just because these tests don't find a pattern doesn't mean there doesn't exist a test that will.
We have in our heads this intuitive idea of "randomness" as being without pattern. But what kind of "pattern" are we to allow here? Could not the data itself be thought of always as a "pattern", just perhaps a highly complex one? "True randomness" is unfortunately not even a well-defined concept, which we would need to have if we can ever hope to have a meaningful discussion on whether or not "true randomness" actually exists.