I'd never come across anyone using this phrase until Nate Silver did. Is it a US English thing?
It doesn’t mean that a broken heart will definitely kill you, just that there is definitely a chance that it will kill you.
I could totally imagine Nate trying to emphasise the idea that 20% is a much bigger probability than say 1% by using language like that, but put too much weight on it here leading to confusion.
What on earth is a professional like Silver doing using the word "extremely" in that context?
If I'd have written that kind of statement in school, the teacher would have crossed it out in red pen and told me to rephrase.
If I toss a coin, are the outcomes "heads" and "tails" both supposed to be "extremely possible", with "landing on its edge" being merely "unlikely"?
(In case you can't already tell) I'm with Taleb on this one.
Extemporizing while being interviewed on TV, presumably to stress the uncertainty in the prediction, is what he is doing, not writing an HN post, tweet or paper.
I suppose that whenever you are interviewed on national TV, you only say exactly what you mean, nothing more and nothing less?
As for the "50/50" here, I don't think this is meant as being exact numbers (after all, the whole point of the issue is that those exact numbers don't really tell you anything, if anything still is possible and any outcome can be justified later), but simply as the common usage of a phrase in the vernacular for "we don't know either way".
The whole point of probabilistic modeling is to replace absolute decisions like "right or wrong" by continuous weights on the possibilities. If you absolutely need a definite decision, you can sample a prediction according to the probability assigned by the model. If the true outcome is x and the model assigned it probability p, then that procedure is going to be wrong (1-p) of the time. You could define that number as the "wrongness" of the probabilistic model, as a continuous analog of the definite case.
The advantage of probabilistic modeling is that you can also ask how wrong the model expects to be and get a meaningful answer. If there are many possible outcomes and none of them very likely, any choice is going to be wrong a lot. But you should expect a good model to have a small difference between its expected and actual wrongness. One might call that value "honesty".
The whole point about the current topic as well as of my post: You missed by about a thousand miles. Please read it again. It's really pointless to argue about a strawman created by you. Your model is useless, that's the point! It makes no real(!) predictions - not usable for anything apart from blowing ever more hot air, and if it doesn't come to pass, you are never wrong because you left the door open by not actually saying anything in the first place.
Do you not understand that the guy/his company did nothing at all? And that giving some arbitrary probability was/is utterly devoid of any meaning (especially if you can't be wrong whatever the actual outcome)? They could have made any prediction at all, what difference would it have made? That is the value of that "work".
However, I realize there's people who like such meaningless drivel. It is a version of appearing to actually do something while not actually doing anything. You make it into the news but you can never be held accountable because whatever happens happens, you just helped create a few more entirely useless headlines (apart from helping with page views and ad impressions of course). It's actually quite ingenious to misuse actually useful tools like statistics.
"Possibility" is like "optimality" in the sense that they are binary attributes and thus don't admin grades. Something either is or isn't possible. Qualifying something as "more possible" is a mistake, and qualifying it as "extremely possible" is just nonsense.
A guy who lives from probabilistic analysis should now better.
I could understand people downvoting my comment (it may be interpreted as harsh even if that wasn't my intention) but... yours? Something weird is at play here.