andrew_gelman_stats andrew_gelman_stats-2010 andrew_gelman_stats-2010-473 knowledge-graph by maker-knowledge-mining
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Introduction: Sciencedaily has posted an article titled Apes Unwilling to Gamble When Odds Are Uncertain : The apes readily distinguished between the different probabilities of winning: they gambled a lot when there was a 100 percent chance, less when there was a 50 percent chance, and only rarely when there was no chance In some trials, however, the experimenter didn’t remove a lid from the bowl, so the apes couldn’t assess the likelihood of winning a banana The odds from the covered bowl were identical to those from the risky option: a 50 percent chance of getting the much sought-after banana. But apes of both species were less likely to choose this ambiguous option. Like humans, they showed “ambiguity aversion” — preferring to gamble more when they knew the odds than when they didn’t. Given some of the other differences between chimps and bonobos, Hare and Rosati had expected to find the bonobos to be more averse to ambiguity, but that didn’t turn out to be the case. Thanks to Sta
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3 Given some of the other differences between chimps and bonobos, Hare and Rosati had expected to find the bonobos to be more averse to ambiguity, but that didn’t turn out to be the case. [sent-4, score-0.686]
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Introduction: Sciencedaily has posted an article titled Apes Unwilling to Gamble When Odds Are Uncertain : The apes readily distinguished between the different probabilities of winning: they gambled a lot when there was a 100 percent chance, less when there was a 50 percent chance, and only rarely when there was no chance In some trials, however, the experimenter didn’t remove a lid from the bowl, so the apes couldn’t assess the likelihood of winning a banana The odds from the covered bowl were identical to those from the risky option: a 50 percent chance of getting the much sought-after banana. But apes of both species were less likely to choose this ambiguous option. Like humans, they showed “ambiguity aversion” — preferring to gamble more when they knew the odds than when they didn’t. Given some of the other differences between chimps and bonobos, Hare and Rosati had expected to find the bonobos to be more averse to ambiguity, but that didn’t turn out to be the case. Thanks to Sta
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Introduction: Suppose you and I agree on a probability estimate…perhaps we both agree there is a 2/3 chance Spain will beat Netherlands in tomorrow’s World Cup. In this case, we could agree on a wager: if Spain beats Netherlands, I pay you $x. If Netherlands beats Spain, you pay me $2x. It is easy to see that my expected loss (or win) is $0, and that the same is true for you. Either of us should be indifferent to taking this bet, and to which side of the bet we are on. We might make this bet just to increase our interest in watching the game, but neither of us would see a money-making opportunity here. By the way, the relationship between “odds” and the event probability — a 1/3 chance of winning turning into a bet at 2:1 odds — is that if the event probability is p, then a fair bet has odds of (1/p – 1):1. More interesting, and more relevant to many real-world situations, is the case that we disagree on the probability of an event. If we disagree on the probability, then there should be
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Introduction: My new Chance ethics column (cowritten with Eric Loken). Click through and take a look. It’s a short article and I really like it. And here’s more Chance.
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Introduction: 1. Suppose that, in a survey of 1000 people in a state, 400 say they voted in a recent primary election. Actually, though, the voter turnout was only 30%. Give an estimate of the probability that a nonvoter will falsely state that he or she voted. (Assume that all voters honestly report that they voted.) P.S. The commenters are picking up some of the unintended “Hare and pineapple” ambiguity in my question!
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Introduction: Sciencedaily has posted an article titled Apes Unwilling to Gamble When Odds Are Uncertain : The apes readily distinguished between the different probabilities of winning: they gambled a lot when there was a 100 percent chance, less when there was a 50 percent chance, and only rarely when there was no chance In some trials, however, the experimenter didn’t remove a lid from the bowl, so the apes couldn’t assess the likelihood of winning a banana The odds from the covered bowl were identical to those from the risky option: a 50 percent chance of getting the much sought-after banana. But apes of both species were less likely to choose this ambiguous option. Like humans, they showed “ambiguity aversion” — preferring to gamble more when they knew the odds than when they didn’t. Given some of the other differences between chimps and bonobos, Hare and Rosati had expected to find the bonobos to be more averse to ambiguity, but that didn’t turn out to be the case. Thanks to Sta
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Introduction: A University of Delaware press release reports : This month, the Journal of Quantitative Analysis in Sports will feature the article “An Estimate of How Hitting, Pitching, Fielding, and Base-stealing Impact Team Winning Percentages in Baseball.” In it, University of Delaware Prof. Charles Pavitt of the Department of Communication defines the perfect “formula” for Major League Baseball (MLB) teams to use to build the ultimate winning team. Pavitt found hitting accounts for more than 45 percent of teams’ winning records, fielding for 25 percent and pitching for 25 percent. And that the impact of stolen bases is greatly overestimated. He crunched hitting, pitching, fielding and base-stealing records for every MLB team over a 48-year period from 1951-1998 with a method no other researcher has used in this area. In statistical parlance, he used a conceptual decomposition of offense and defense into its component parts and then analyzed recombinations of the parts in intuitively mea
Introduction: I recently wrote about the difficulty people have with probabilities, in this case the probability that Obama wins the election. If the probability is reported as 70%, people think Obama is going to win. Actually, though, it just means that Obama is predicted to get about 50.8% of the two-party vote, with an uncertainty of something like 2 percentage points. So, as I wrote, the election really is too close to call in the sense that the predicted vote margin is less than its uncertainty. But . . . when people see a number such as 70%, they tend to attribute too much certainty to it. Especially when the estimated probability has increased from, say 60%. How to get the point across? Commenter HS had what seems like a good suggestion: Say that Obama will win, but there is 25% chance (or whatever) that this prediction is wrong? Same point, just slightly different framing, but somehow, this seems far less incendiary. I like that. Somehow a stated probability of 75% sounds a
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Introduction: Sciencedaily has posted an article titled Apes Unwilling to Gamble When Odds Are Uncertain : The apes readily distinguished between the different probabilities of winning: they gambled a lot when there was a 100 percent chance, less when there was a 50 percent chance, and only rarely when there was no chance In some trials, however, the experimenter didn’t remove a lid from the bowl, so the apes couldn’t assess the likelihood of winning a banana The odds from the covered bowl were identical to those from the risky option: a 50 percent chance of getting the much sought-after banana. But apes of both species were less likely to choose this ambiguous option. Like humans, they showed “ambiguity aversion” — preferring to gamble more when they knew the odds than when they didn’t. Given some of the other differences between chimps and bonobos, Hare and Rosati had expected to find the bonobos to be more averse to ambiguity, but that didn’t turn out to be the case. Thanks to Sta
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Introduction: A University of Delaware press release reports : This month, the Journal of Quantitative Analysis in Sports will feature the article “An Estimate of How Hitting, Pitching, Fielding, and Base-stealing Impact Team Winning Percentages in Baseball.” In it, University of Delaware Prof. Charles Pavitt of the Department of Communication defines the perfect “formula” for Major League Baseball (MLB) teams to use to build the ultimate winning team. Pavitt found hitting accounts for more than 45 percent of teams’ winning records, fielding for 25 percent and pitching for 25 percent. And that the impact of stolen bases is greatly overestimated. He crunched hitting, pitching, fielding and base-stealing records for every MLB team over a 48-year period from 1951-1998 with a method no other researcher has used in this area. In statistical parlance, he used a conceptual decomposition of offense and defense into its component parts and then analyzed recombinations of the parts in intuitively mea
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