3 ms·
> When you have enough data to suggest your model is wrong, you should throw away the model and the assumptions it's based on, not the data. In the example, yo
by moring 6y ago
> When you have enough data to suggest your model is wrong, you should throw away the model and the assumptions it's based on, not the data.
In the example, you agreed to assume the coin is fair. That is, you agreed to stick to the model even if it turns out to be wrong. Throwing the model away, however suggested by the evidence, is just violating the rules you agreed to.
- greggman3 6y agoI'd call it the opposite. There is statistically no such thing as 99 heads in row. So when someone says "assume the coin toss is fair, 99 heads in row happen, what are the odds the next will be heads". Your answer should not be "50%". Your answer should be "You just asked me a bullshit question because there is basically no such thing as 99 heads in a row in the real word. In a hypothetical world maybe but in the real world nope" Or maybe to be nicer about it the proper response is "So are we talking about the real world because in the real world the odds of 99 heads in a row are 1 in 2^100 which is effectively zero so I just want to verify you understand that your hypothetical situation and conclusion will have no basis in reality. If you actually saw 99 heads in a row then the odds are infinitely higher the coin is broken and is not fair"