I was wondering if I’ve interpreted this correctly:
‘For a true Bayesian, it is impossible to seek evidence that confirms a theory. There is no possible plan you can devise, no clever strategy, no cunning device, by which you can legitimately expect your confidence in a fixed proposition to be higher (on average) than before. You can only ever seek evidence to test a theory, not to confirm it.’
Does this mean that it is impossible to prove the truth of a theory? Because the only evidence that can exist is evidence that falsifies the theory, or supports it?
For example, something people know about gravity and objects under it’s influence, is that on Earth objects will accelerate at something like 9.81ms^-2. If we dropped a thousand different objects and observed their acceleration, and found it to be 9.81ms^-2, we would have a thousand pieces of evidence supporting the theory, and zero pieces to falsify the theory. We all believe that 9.81 is correct, and we teach that it is the truth, but we can never really know, because new evidence could someday appear that challenges the theory, correct?
It is correct that we can never find enough evidence to make our certainty of a theory to be exactly 1 (though we can get it very close to 1). If we were absolutely certain in a theory, then no amount of counterevidence, no matter how damning, could ever change our mind.
“For a true Bayesian, it is impossible to seek evidence that confirms a theory”
The important part of the sentence here is seek. The isn’t about falsificationism, but the fact that no experiment you can do can confirm a theory without having some chance of falsifying it too. So any observation can only provide evidence for a hypothesis if a different outcome could have provided the opposite evidence.
For instance, suppose that you flip a coin. You can seek to test the theory that the result was HEADS, by simply looking at the coin with your eyes. There’s a 50% chance that the outcome of this test would be “you see the HEADS side”, confirming your theory (p(HEADS | you see HEADS) ~ 1). But this only works because there’s also a 50% chance that the outcome of the test would have shown the result to be TAILS, falsifying your theory (P(HEADS | you see TAILS) ~ 0). And in fact there’s no way to measure the coin so that one outcome would be evidence in favour of HEADS (P(HEADS | measurement) > 0.5), without the opposite result being evidence against HEADS (P(HEADS | ¬measurement) < 0.5).
Hi, new here.
I was wondering if I’ve interpreted this correctly:
‘For a true Bayesian, it is impossible to seek evidence that confirms a theory. There is no possible plan you can devise, no clever strategy, no cunning device, by which you can legitimately expect your confidence in a fixed proposition to be higher (on average) than before. You can only ever seek evidence to test a theory, not to confirm it.’
Does this mean that it is impossible to prove the truth of a theory? Because the only evidence that can exist is evidence that falsifies the theory, or supports it?
For example, something people know about gravity and objects under it’s influence, is that on Earth objects will accelerate at something like 9.81ms^-2. If we dropped a thousand different objects and observed their acceleration, and found it to be 9.81ms^-2, we would have a thousand pieces of evidence supporting the theory, and zero pieces to falsify the theory. We all believe that 9.81 is correct, and we teach that it is the truth, but we can never really know, because new evidence could someday appear that challenges the theory, correct?
Thanks
It is correct that we can never find enough evidence to make our certainty of a theory to be exactly 1 (though we can get it very close to 1). If we were absolutely certain in a theory, then no amount of counterevidence, no matter how damning, could ever change our mind.
The important part of the sentence here is seek. The isn’t about falsificationism, but the fact that no experiment you can do can confirm a theory without having some chance of falsifying it too. So any observation can only provide evidence for a hypothesis if a different outcome could have provided the opposite evidence.
For instance, suppose that you flip a coin. You can seek to test the theory that the result was
HEADS, by simply looking at the coin with your eyes. There’s a 50% chance that the outcome of this test would be “you see theHEADSside”, confirming your theory (p(HEADS | you see HEADS) ~ 1). But this only works because there’s also a 50% chance that the outcome of the test would have shown the result to beTAILS, falsifying your theory (P(HEADS | you see TAILS) ~ 0). And in fact there’s no way to measure the coin so that one outcome would be evidence in favour ofHEADS(P(HEADS | measurement) > 0.5), without the opposite result being evidence againstHEADS(P(HEADS | ¬measurement) < 0.5).