Oh, I missed that. In that case, yeah the evidence went from good to very strong.

As for science, my main point is that we need to embrace the noise. A lot of people try to use metrics to reduce the noise (which is good) but have this fundamental belief that noise can be absolutely removed. But this is a deep misunderstanding of what noise is. Ignoring the probabilistic nature of the universe, even a fully deterministic system has noise. A good example is the lack of absolute precision. Statistics captures error and helps us analyze that error. But error always exists due to the lack of both omniscience and omnipotence. In an nondeterministic world, it just becomes more important to embrace it. To acknowledge that it is a fundamental part of not just the model, but the thing we're modeling. In this respect, removing a noise variable doesn't just result in overconfident results, but missing a fundamental variable. Of course, noise can also be used to capture unknowns. So in any case, the noise has the be embraced. We should reduce it as much as possible, but it can't ever be removed. That's the problem: people are removing noise from their models (e.g. using a benchmark to determine evaluation absolutely)