It's a good start to realize that what underlies all the understanding of science are simplified predictive models, and usually only statistical models at that.

It's a good start to realize that what underlies all the understanding of science are simplified predictive models, and usually only statistical models at that. What this means is that running an experiment in many fields is so difficult that replication is a real challenge. There are so MANY ways you can screw up, or you could just have a statistical fluke that screws you over. Just a tiny contamination or seemingly irrelevant missed step will cause a failure. That's why the idea of having journals composed of failed experiments just doesn't work. Unstated experimental process assumptions are legion. Sometimes an expert can look at the result and see what you've done wrong (like bad contacts in "Electron Band Structure In Germanium, My Ass") and often not even that. Sometimes there's something interesting in the failure, but 99% of the time it's just your pitch is so bad you can't hit the strike zone. Do better! The things that are easy to replicate (and usually they've been specifically designed that way like Starbucks' over roasted beans), have actually been reduced to engineering. They're not on the edge where scientists can get published. That way perverse incentive madness lies. Enjoy the controlability of inputs, the repeatability of bugs, the near perfection of compilers and memory allocation, the complete independence of variables while you can. Unless that is, you like Rowhammer and voltage glitch attacks.