Suppose Adam has a theory that goblins exist in his attic. He hears noises coming from the attic even though he knows noone is in there. He realizes that even though it is possible that goblins in the attic may be causing noise, he has no reason to think so. Other more natural accounts are more likely.
Suppose John, on the other hand, hears noises in the attic. From these noises, he postulates whether goblins are in the attic making noise. He again, after wondering this, makes the conclusion that natural accounts are more likely.
Although on the face of it the theory seems ridiculous either way, something about the second account is different. It was created to accommodate the data. In the former case, the theory was created before the data, even though technically, the theory doesn't actually predict the data (although in this case, the prediction may not make a difference to the conclusion).
Bayesians often argue that a theory's construction before data often is evidence of higher prior probability. But by this logic, does that imply that technically, the P (H|D) with H = goblins, and D = noise, is higher in the former case, even though both seem equally ridiculous?