Check out Imre Lakatos. "Falsifiability" is too strict since "every model is wrong". Lakatos calls that Popper0, which is not what Popper ever advocated. In a

Check out Imre Lakatos. "Falsifiability" is too strict since "every model is wrong". Lakatos calls that Popper0, which is not what Popper ever advocated. In a nutshell: The real thing going on is you are supposed to use Bayes' rule, where you normalize how well your explanation explains the observations ( p(H[0])p(H[0]|D) ) to the sum of all known explanations ( p(H[0:n])p(H[0:n]|D) ). Thus if you have a vague explanation that can fit anything, it won't be much better than a million other possibilities so who cares. We only care when someone comes up with an accurate prediction that would be otherwise surprising. There are many more interesting ideas too (eg, "degenerating research programmes"). Edit: I should also emphasize that my main point above is that there is a misuse of statistics going on though.