I absolutely agree that it's best to be aware of any potential confirmation bias. Nevertheless, we shan't arrive at a statically significant result without statistically relevant data. The best arrival point: "observation" of <behavior> at [given current game-state]
Given this game-example, confirmation bias is purely subjective. Thanks
How are you still dying on this hill?
Confirmation bias can’t be subjectively
occurring. Someone either is or is not experiencing confirmation bias.
The argument you’re presenting is:
P1 there is some unknown modifier or modifiers, therefore
C1 it is possible that said modifiers were coinciding such that only one possible advisor type could be available, therefore
C2 it is possible that a person who reports firing and receiving the same advisor type over and over is not experiencing confirmation bias.
That’s a frivolous claim to argue to this extent: possibility
isn’t interesting or meaningful; likelihood
is worth discussing. Notwithstanding it’s meaningless, I’m happy to concede it’s perfectly true.
I hate the fact that if you fire an advisor it seems like you always get the advisor you just fired over and over and over again. I don't think this is confirmation bias because I've tested it multiple times.
But the original claim is that “it seems like you always” get the same advisor you fired “over and over”. Not in any specific case, but always
. Not “you might possibly sometimes”, always
. This is plainly untrue (and I’m sure conscious hyperbole on the part of the author), and two tests have now been run demonstrating that it’s untrue.
The possibility that as you’ve exhaustively and pointlessly argued it might sometimes
occur that this happens does not imply that you “always” get the same advisor you just fired “over and over”. It implies that this might (but also might not) sometimes occur. We don’t need a statistically significant event record to demonstrate that the user claiming an event “always” happens is wrong (and probably not being serious); they are experiencing confirmation bias.