andrew_gelman_stats andrew_gelman_stats-2012 andrew_gelman_stats-2012-1242 knowledge-graph by maker-knowledge-mining
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Introduction: Kansas Man Does Not Win Lottery, Is Struck By Lightning . Finally, a story that gets the probabilities right.
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same-blog 1 1.0 1242 andrew gelman stats-2012-04-03-Best lottery story ever
Introduction: Kansas Man Does Not Win Lottery, Is Struck By Lightning . Finally, a story that gets the probabilities right.
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Introduction: I spoke at the University of Kansas the other day. Kansas is far away so I gave the talk by video. We did it using a G+ hangout, and it worked really well, much much better than when I gave a talk via Skype . With G+, I could see and hear the audience clearly, and they could hear me just fine while seeing my slides (or my face, I went back and forth). Not as good as a live presentation but pretty good, considering. P.S. And here’s how to do it! Conflict of interest disclaimer: I was paid by Google last year to give a short course.
3 0.14431222 2346 andrew gelman stats-2014-05-24-Buzzfeed, Porn, Kansas…That Can’t Be Good
Introduction: This post is by David K. Park and courtesy of Alex Palen Ellis… Thought you might find this funny: Buzzfeed set out to study porn consumption versus the red/blue political spectrum. And they failed miserably. An article form opennews.org outlines six major fallacies Buzzfeed committed, the best of which resulted in the Kansas effect: “Pornhub’s writeup omitted any explicit description of their methodology—this is never a good sign—but it seems to have involved mapping the IP addresses from which users visited the site to physical addresses and reverse geocoding those to get states…. a large percentage of IP addresses could not be resolved to an address any more specific than “USA.” When that address was geocoded, it returned a point in the centroid of the continental United States, which placed it in the state of—you guessed it—Kansas!” As a result, Kansas was 2.95 std dev above the mean. Those pervs! from: https://source.opennews.org/en-US/learning/distrust-your-data/
4 0.13439566 2262 andrew gelman stats-2014-03-23-Win probabilities during a sporting event
Introduction: Todd Schneider writes: Apropos of your recent blog post about modeling score differential of basketball games , I thought you might enjoy a site I built, gambletron2000.com , that gathers real-time win probabilities from betting markets for most major sports (including NBA and college basketball). My original goal was to use the variance of changes in win probabilities to quantify which games were the most exciting, but I got a bit carried away and ended up pursuing a bunch of other ideas, which you can read about in the full writeup here This particular passage from the anonymous someone in your post: My idea is for each timestep in a game (a second, 5 seconds, etc), use the Vegas line, the current score differential, who has the ball, and the number of possessions played already (to account for differences in pace) to create a point estimate probability of the home team winning. reminded me of a graph I made, which shows the mean-reverting tendency of N
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Introduction: Christian points me to this amusing story by Jonah Lehrer about Mohan Srivastava, (perhaps the same person as R. Mohan Srivastava, coauthor of a book called Applied Geostatistics) who discovered a flaw in a scratch-off game in which he could figure out which tickets were likely to win based on partial information visible on the ticket. It appears that scratch-off lotteries elsewhere have similar flaws in their design. The obvious question is, why doesn’t the lottery create the patterns on the tickets (including which “teaser” numbers to reveal) completely at random? It shouldn’t be hard to design this so that zero information is supplied from the outside. in which case Srivastava’s trick would be impossible. So why not put down the numbers randomly? Lehrer quotes Srivastava as saying: The tickets are clearly mass-produced, which means there must be some computer program that lays down the numbers. Of course, it would be really nice if the computer could just spit out random
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Introduction: Kansas Man Does Not Win Lottery, Is Struck By Lightning . Finally, a story that gets the probabilities right.
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Introduction: Todd Schneider writes: Apropos of your recent blog post about modeling score differential of basketball games , I thought you might enjoy a site I built, gambletron2000.com , that gathers real-time win probabilities from betting markets for most major sports (including NBA and college basketball). My original goal was to use the variance of changes in win probabilities to quantify which games were the most exciting, but I got a bit carried away and ended up pursuing a bunch of other ideas, which you can read about in the full writeup here This particular passage from the anonymous someone in your post: My idea is for each timestep in a game (a second, 5 seconds, etc), use the Vegas line, the current score differential, who has the ball, and the number of possessions played already (to account for differences in pace) to create a point estimate probability of the home team winning. reminded me of a graph I made, which shows the mean-reverting tendency of N
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: Christian points me to this amusing story by Jonah Lehrer about Mohan Srivastava, (perhaps the same person as R. Mohan Srivastava, coauthor of a book called Applied Geostatistics) who discovered a flaw in a scratch-off game in which he could figure out which tickets were likely to win based on partial information visible on the ticket. It appears that scratch-off lotteries elsewhere have similar flaws in their design. The obvious question is, why doesn’t the lottery create the patterns on the tickets (including which “teaser” numbers to reveal) completely at random? It shouldn’t be hard to design this so that zero information is supplied from the outside. in which case Srivastava’s trick would be impossible. So why not put down the numbers randomly? Lehrer quotes Srivastava as saying: The tickets are clearly mass-produced, which means there must be some computer program that lays down the numbers. Of course, it would be really nice if the computer could just spit out random
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