precognition experiments.
...
The Bayesian charge is that by refusing to conditionalize on the actual data available to her, the frequentist has missed important information: specifically, that the mean of the distribution is definitely between 0.1 and 1.0.
Still sounds like silly terminology collision. Like if in the physics you had right-hand-rulers and left-hand-rulers and some would charge that the direction of magnetic field is all wrong, while either party simply means different thing by 'magnetic field' (and a few people associated with insane clown posse sometimes straight calculate it wrong)
edit: ohh what you wrote is even worse than sense I accidentally read into it (misreading uniform as normal getting confused afterwards). Picking people screw up math as strawman. Stupid, very stupid. And boring.
Nowhere does it follow from seeing the probability as a limit in the infinite number of trials (frequentism), that the mean of that distribution with unknown mean wouldn't be restricted to specific range. Say, you draw 1 number from this distribution with width 1. It immediately follows that the values of unknown parameter of the generator of the number, that fall outside x-0.5 , x+0.5 are not possible. You keep drawing more, you narrow down the set of possible values. [i am banning the 'mean' because there's the mean that is the property of the system we are studying, and there is the mean of the model we are creating, 2 different things]
Nowhere does it follow from seeing the probability as a limit in the infinite number of trials (frequentism), that the mean of that distribution with unknown mean wouldn't be restricted to specific range.
In the particular case I gave, of course frequentists could produce an argument that the mean must be in the given range. But this could not be a statistical argument, it would have to be a deductive logical argument. And the only reason a deductive argument works here is that the posterior of the mean being in the given range is 1. If it were only slig...
I've had a bit of success with getting people to understand Bayesianism at parties and such, and I'm posting this thought experiment that I came up with to see if it can be improved or if an entirely different thought experiment would be grasped more intuitively in that context:
I originally came up with this idea to explain falsifiability which is why I didn't go with say the example in the better article on Bayesianism (i.e. any other number besides a 3 rolled refutes the possibility that the trick die was picked) and having a hypothesis that explains too much contradictory data, so eventually I increase the sides that the die has (like a hypothetical 50-sided die), the different types of die in the jar (100-sided, 6-sided, trick die), and different distributions of die in the jar (90% of the die are 200-sided but a 3 is rolled, etc.). Again, I've been discussing this at parties where alcohol is flowing and cognition is impaired yet people understand it, so I figure if it works there then it can be understood intuitively by many people.