101–110 of 116 posts
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0 in 10 is still "no more than 1 in 10", so I don't see why that's scary.
0.9 in 10 is also no more than 1 in 10.
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>Hillary Clinton had a >70% of winning the US presidential election Probabilities without confidence intervals[1] are by-and-large meaningless (She has a 90% chance of of winning with a confidence interval of +11% -100%). No amount of d3.js on 538's blog will change this. https://en.wikipedia.org/wiki/Confidence_interval
> Probabilities without confidence intervals are by-and-large meaningless. That's just not true. If I believe that my team has a 20% chance to win and you offer me a bet with anything better than 5-to-1 odds I should take the bet. If you offer me anything worse than 5-to-1, then I should not take the bet. There's no fuzz factor necessary; no confidence interval that I need to use to make the decision. Perhaps you're…
You would leap at a bet with 10000-1 odds, and never go for 2-1.
Let's say you can make these bets repeatedly. Would you always bet on 5.01-1 odds and never on 4.99-1 odds?
Is it that odd to think that your team has about a 20% chance to win?
"Lesson 1: Use probabilities instead of words to avoid misinterpretation." Probabilities are meaningless unless it’s a repeatable experiment otherwise its a ludic fallacy eg "There's a 70% chance of Hillary winning". This is an un-provable statement. Either she wins and prediction was right, or she loses and it counts as part of the 30%. This is Nate Silver's get-out-of-jail-free card so even when he's wrong he comes…
> Probabilities are meaningless unless it’s a repeatable experiment otherwise its a ludic fallacy Um... this goes against the entirety of the Bayesian approach to statistics. I think you'd find a lot of very intelligent people who disagree strongly with this statement. The Bayesian approach takes probabilities as subjective confidences. You can describe confidences as "well calibrated" if, when you look at their hist…
I never claimed to be intelligent.
" You can describe confidences as "well calibrated" if, when you look at their historical guesses, if their 70% assessments are correct 70% of the time."
- Again, if you're trying to figure out if a coin is split 50/50 then yes, but without being able to repeat the same experiment you're fooling yourself and the whole aspect of bayesian thinking I think goes out the window. eg me being right about unrelated topics doesn't mean I'm right/wrong about specific topics.
"Lesson 1: Use probabilities instead of words to avoid misinterpretation." Probabilities are meaningless unless it’s a repeatable experiment otherwise its a ludic fallacy eg "There's a 70% chance of Hillary winning". This is an un-provable statement. Either she wins and prediction was right, or she loses and it counts as part of the 30%. This is Nate Silver's get-out-of-jail-free card so even when he's wrong he comes…
It is true that you cannot evaluate a single probabilistic prediction as being right or wrong (so you cannot say that Nate Silver was either wrong or right in some example - he gave a probability, and that probability was either accurate or inaccurate, but in isolation that cannot be evaluated). However, if a consistent process is used to generate a sequence of probabilistic predictions, the accuracy of the predictio…
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Pascals' Wager: multiply the odds by the cost or benefit of each outcome. The higher the potential cost, the less good your odds look even if the percentage is the same.
Not sure what that has to do with Pascal’s wager? This is just basic “expected value” in probability terms.
Plus wasn't it pascal who invented expected value?
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The analysis after the elections was interesting-- think pieces asking things like "How could the stats have been so wrong?!". A fair dice has a roughly 83% chance of landing on a number between 1 and 5, but if you roll a 6 you don't ask yourself the same question.
People are mixing up % of vote with likelihood of victory. If she was polling at 70% that would be an almost 100% chance of winning because a 20% swing is unlikely. However a 70% chance of victory is much much closer.
It depends on the context. Hillary Clinton had a >70% of winning the US presidential election according to the most responsible analyses (see 538: https://projects.fivethirtyeight.com/2016-election-forecast/ ). Most folks took 70% to mean that she would certainly win and were bitterly disappointed the morning after. On the other hand no sane person would (willingly) play Russian roulette with a 70% or even 5 of 6 cha…
> Most folks took 70% to mean that she would certainly win and were bitterly disappointed the morning after. Surely, given the outcome, more of those folks were pleasantly surprised at the outcome than "bitterly disappointed".
From my point of view in Philadelphia, however, virtually everyone was disappointed modulo a few crazies or people who had been hiding under a rock or some closeted college republicans.
This just goes to show how bifurcated America has become, people on opposite sides view the other side as incomprehensibly batshit-crazy and/or evil.
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> In everyday life, one conflates probability with severity of outcome. My everyday example for that is the weather forecast and the question 'will it rain' often answered with a precipitation probability? - Probability: How likely is it that I will be hit by at least one rain drop - Severity: How many rain drops will hit me It sounds a little abstract, but whenever I see some everyday weather forecast I wonder what…
If you ask Alexa, "will it rain today?" she'll respond, "it probably won't rain today" for any probability below 50%. I always thought this was an interesting and illustrative example. If there's a 40% chance of rain, I wouldn't in casual conversation say that, "it probably won't rain today."
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It is true that you cannot evaluate a single probabilistic prediction as being right or wrong (so you cannot say that Nate Silver was either wrong or right in some example - he gave a probability, and that probability was either accurate or inaccurate, but in isolation that cannot be evaluated). However, if a consistent process is used to generate a sequence of probabilistic predictions, the accuracy of the predictio…
Absolutely - and my issue is conflating experiments that ARE repeatable (eg a probabalistic game of chance) with experiments that arent (eg election results of two particular candidates). So its one of those heads I win tails you lose bets when it comes to Nate
The more that Nate Silver produces new prior probability estimates for events, the better an idea we can get of the accuracy of his process. This measure is calibration, as the original article mentions.
If our oracle tells us that an event has a 70% chance of happening, then regardless of whether the event happens or not we can't say much about the oracle's calibration. But if, out of 100 independent events the oracle has predicted to each have a 70% chance of happening, 69 of those events actually happened, then that tells us quite a bit.
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Not sure what that has to do with Pascal’s wager? This is just basic “expected value” in probability terms.
The cost of "believing" in God, near zero, the expected value if correct - huge :-) Plus wasn't it pascal who invented expected value?
And yes he was the inventor.