andrew_gelman_stats andrew_gelman_stats-2012 andrew_gelman_stats-2012-1556 knowledge-graph by maker-knowledge-mining
Source: html
Introduction: 1. I can simultaneously (a) accept that Obama has a 72 percent chance of winning and (b) say the election is too close to call 2. Michael’s a Republican, Susan’s a Democrat 3. The narcissism of the narcissism of small differences 4. Obamanomics: A Counter-counterhistory 5. Not a gaffe 6. Categories influence predictions about individual consistency
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same-blog 1 1.0 1556 andrew gelman stats-2012-11-01-Recently in the sister blogs: special pre-election edition!
Introduction: 1. I can simultaneously (a) accept that Obama has a 72 percent chance of winning and (b) say the election is too close to call 2. Michael’s a Republican, Susan’s a Democrat 3. The narcissism of the narcissism of small differences 4. Obamanomics: A Counter-counterhistory 5. Not a gaffe 6. Categories influence predictions about individual consistency
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: Susan points me to this . But I don’t really see the point. Simply leaning the bike against the wall seems like a better option to me.
4 0.098565012 1574 andrew gelman stats-2012-11-12-How to Lie With Statistics example number 12,498,122
Introduction: This post is by Phil Price. Bill Kristol notes that “Four presidents in the last century have won more than 51 percent of the vote twice: Roosevelt, Eisenhower, Reagan and Obama”. I’m not sure why Kristol, a conservative, is promoting the idea that Obama has a mandate, but that’s up to him. I’m more interested in the remarkable bit of cherry-picking that led to this “only four presidents” statistic. There was one way in which Obama’s victory was large: he won the electoral college 332-206. That’s a thrashing. But if you want to claim that Obama has a “popular mandate” — which people seem to interpret as an overwhelming preference of The People such that the opposition is morally obligated to give way — you can’t make that argument based on the electoral college, you have to look at the popular vote. That presents you with a challenge for the 2012 election, since Obama’s 2.7-point margin in the popular vote was the 12th-smallest out of the 57 elections we’ve had. There’s a nice sor
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Introduction: Paul Alper writes: Hi Andrew (or Andy or even Gelman [17 of them]): Go to this link and have some fun with (useless? powerful?) data mining. As the authors say, it is addictive. Paul (no other way to spell it) Alper [215 of us] I’m reminded of this discussion from 2012, “Michael’s a Republican, Susan’s a Democrat.” As I wrote at the time: It’s no surprise that men give more to Republicans and women to Democrats, or that the average contribution to a Republican has a larger dollar value than the average contribution to a Democrat, nor perhaps should we be surprised that “Tom” splits his support between the two parties while “Thomas” is a strong Republican. Still, it’s fun to see the data. Overall, I think this graph understates contributions to Republicans because it doesn’t include those new super-pacs. But the new tool seems to be based on a different dataset, opinion polls rather than campaign contributions. Playing around a bit, I see a lot less variability
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20 0.065884918 391 andrew gelman stats-2010-11-03-Some thoughts on election forecasting
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same-blog 1 0.99504244 1556 andrew gelman stats-2012-11-01-Recently in the sister blogs: special pre-election edition!
Introduction: 1. I can simultaneously (a) accept that Obama has a 72 percent chance of winning and (b) say the election is too close to call 2. Michael’s a Republican, Susan’s a Democrat 3. The narcissism of the narcissism of small differences 4. Obamanomics: A Counter-counterhistory 5. Not a gaffe 6. Categories influence predictions about individual consistency
2 0.84381711 1574 andrew gelman stats-2012-11-12-How to Lie With Statistics example number 12,498,122
Introduction: This post is by Phil Price. Bill Kristol notes that “Four presidents in the last century have won more than 51 percent of the vote twice: Roosevelt, Eisenhower, Reagan and Obama”. I’m not sure why Kristol, a conservative, is promoting the idea that Obama has a mandate, but that’s up to him. I’m more interested in the remarkable bit of cherry-picking that led to this “only four presidents” statistic. There was one way in which Obama’s victory was large: he won the electoral college 332-206. That’s a thrashing. But if you want to claim that Obama has a “popular mandate” — which people seem to interpret as an overwhelming preference of The People such that the opposition is morally obligated to give way — you can’t make that argument based on the electoral college, you have to look at the popular vote. That presents you with a challenge for the 2012 election, since Obama’s 2.7-point margin in the popular vote was the 12th-smallest out of the 57 elections we’ve had. There’s a nice sor
3 0.77752548 1569 andrew gelman stats-2012-11-08-30-30-40 Nation
Introduction: Barack Obama’s win has a potentially huge effect on policy. The current budget negotiations will affect the level and direction of government spending and on the mix of taxes paid by different groups of Americans. We can guess that a President Romney would have fought hard against upper-income tax increases. Other areas of long-term impact include the government’s stance on global warming, foreign policy, and the likelihood that Obama will nominate new Supreme Court justices who will uphold the right to abortion announced in Roe v. Wade. When it comes to public opinion, the story is different. The Democrats may well benefit in 2014 and 2016 from the anticipated slow but steady recovery of the economy over the next few years—but, as of November 6, 2012, the parties are essentially tied, with Barack Obama receiving 51% of the two-party vote, compared to Mitt Romney’s 49%, a split comparable to Al Gore’s narrow victory in 2000, Richard Nixon’s in 1968, and John Kennedy’s in 1960.
4 0.76185012 1075 andrew gelman stats-2011-12-20-This guy has a regular column at Reuters
Introduction: Gregg Easterbrook : Gingrich is a wild card. He probably would end up a flaming wreckage in electoral terms, but there’s a chance he could become seen as the man unafraid to bring sweeping change to an ossified Washington, D.C. There’s perhaps a 90 percent likelihood Obama would wipe the floor with Gingrich, versus a 10 percent likelihood Gingrich would stage an historic upset. This is the dumbest thing I’ve seen since . . . ummm, I dunno, how bout this ? It actually gets worse because Easterbrook then invokes game theory. What next? Catastrophe theory? Intelligent design? P.S. Maybe I should explain for readers without an education in probability theory. Let’s suppose “wipe the floor” means that Obama gets 55%+ of the two-party vote, and let’s suppose that “an historic upset” means that Obama gets less than 50% of the vote. Now try to draw a forecast distribution that has 90% of its probability above 0.55 and 10% of it’s probability below 0.50. It’s a pretty weird-loo
5 0.75150967 1540 andrew gelman stats-2012-10-18-“Intrade to the 57th power”
Introduction: David Pennock writes: http://PredictWiseQ.com is our (beta) prediction contest which aims to estimate not just the marginal probabilities of election outcomes this November, but millions of correlations among outcomes as well, like the chance Obama will win both Ohio and Florida, or the chance Romney will win if the September jobs numbers are negative. It’s a working example of a combinatorial prediction market design we published this summer in the conference ACM EC’12. And here’s Pennock’s blog, which supplies more background.
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1 0.91179687 1249 andrew gelman stats-2012-04-06-Thinking seriously about social science research
Introduction: I haven’t linked to the Baby Name Wizard in awhile. . . . Laura Wattenberg takes a look at the question , “Does a hard-to-pronounce baby name hurt you?” Critical thinking without “debunking”—this is the way to go.
2 0.90465343 26 andrew gelman stats-2010-05-11-Update on religious affiliations of Supreme Court justices
Introduction: When Sonia Sotomayor was nominated for the Supreme Court, and there was some discussion of having 6 Roman Catholics on the court at the same time, I posted the following historical graph: It’s time for an update: It’s still gonna take awhile for the Catholics to catch up. . . . And this one might be relevant too: It looks as if Jews and men have been overrepresented, also Episcopalians (which, as I noted earlier, are not necessarily considered Protestant in terms of religious doctrine but which I counted as such for the ethnic categorization). Religion is an interesting political variable because it’s nominally about religious belief but typically seems to be more about ethnicity.
3 0.88282692 425 andrew gelman stats-2010-11-21-If your comment didn’t get through . . .
Introduction: It probably got caught in the spam filter. We get tons and tons of spam (including the annoying spam that I have to remove by hand). If your comment was accompanied by an ad or a spam link, then maybe I just deleted it.
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Introduction: Good stuff , as always, from Laura Wattenberg.
Introduction: The course outline ZombieCourseOutline.rtf Hints/draft R code for implementing this for a regression example from D. Pena x=c(1:10,17,17,17) y=c(1:10,25,25,25) ZombieAssign1.txt The assignment being to provide a legend that explains all the lines and symbols in this plot ZombieAssign1.pdf With a bonus assignment being to provide better R code and or techniques. And a possible graduate student assignment to investigate what percentage of examples in graduate stats texts (e.g. Cox & Hinkley) could be displayed this way (reducing the number of parameters to least number possible). K? p.s. might have been a better post for Friday the 13th p.s.2 background material from my thesis (passed in 2007) ThesisReprint.pdf
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