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Published 16.05.2015 | Author : admin | Category : Things Guys Love

Just following up on our post the other day on retrospective evaluations of probabilistic predictions: For more on Leicester City, see Nick Goff on Why did bookmakers lose on Leicester?
The post Nick and Nate and Mark on Leicester and Trump appeared first on Statistical Modeling, Causal Inference, and Social Science. For more on Trump, see Nate Silver on How I Acted Like A Pundit And Screwed Up On Donald Trump and Mark Palko making a lot of these points, back in September, 2015, on how journalists were getting things wrong and not updating their models given the evidence in front of them. Which is closely related to the “Why did bookmakers lose on Leicester?, because it seems that their big losses came not from those 5000-1 preseason odds but from bets during the early part of the season, when Leicester was winning and winning but the bookies were lowering the odds only gradually, not fully accounting for the information provided by these games.
Jonathan Sterne sent me this opinion piece by Stephan Lewandowsky and Dorothy Bishop, two psychology researchers who express concern that the movement for science and data transparency has been abused.
We strongly support open data, and scientists should not regard all requests for data as harassment. The status of data availability should be enshrined in the publication record along with details about what information has been withheld and why. Scientific publications should be seen as a€?living documentsa€™, with corrigenda an accepted a€” if unwelcome a€” part of scientific progress. Freedom-of-information (FOI) requests have revealed conflicts of interest, including undisclosed funding of scientists by corporate interests such as pharmaceutical companies and utilities.
Journals and institutions can also publish threats of litigation, and use sunlight as a disinfectant.
Issues such as reproducibility and conflicts of interest have legitimately attracted much scrutiny and have stimulated corrective action. Research is already moving towards study a€?pre-registrationa€™ (researchers publishing their intended method and analysis plans before starting) as a way to avoid bias, and the same strictures should apply to critics during reanalysis.
I would have appreciated the chance to explain the analytical procedure in much more detail than was possible during my 10-minute talk but he didna€™t give me the option. There was time for a press release with an interview and a publicity photo, but no time for writing up the method or posting the data.
The post Bias against women in academia appeared first on Statistical Modeling, Causal Inference, and Social Science.
All challenges aside, there are disparities, for whatever reasons, between men and women in the workforce, and it’s a topic worth studying, especially given how the roles of men and women have changed in recent decades. I have no reason to think that academia is worse than other sectors when it comes to how women are treated. Tian Zheng pointed me to this page by Virginia Valian, author of the 1988 book, “Why So Slow?
The post Birthday analysis—Friday the 13th update, and some model checking appeared first on Statistical Modeling, Causal Inference, and Social Science. You can see dips on the 13th of each month, also a slight bump on Valentine’s, and drops on several other special days, including February 29th, Halloween, and September 11th, along with a bunch of major holidays. If you look carefully on the bottom graph you’ll again see the little dips on the 13th of each month, but the blow-up above is better. Comparing to the results from the earlier time period (just look at your copy of BDA3!), we see the new pattern on September 11th, also in general the day-of-week effects and special-day effects are much higher. The first thing I noticed, after seeing the big dips on New Year’s, July 4th, and Christmas, were the blurs around Labor Day, Memorial Day, and Thanksgiving.
The second thing is that the day-of-week effects clearly go up over time, and we know from our separate analyses of the 1968-2008 and 2000-2014 data that the individual-date and special-day effects increase over time too.
The other problem with the model we fit can be seen, for example, around Christmas and New Year’s. You can also see this problem arising in the last half of September, where there seem to be “too many babies” for two entire weeks! This is a great example of posterior model checking, and a reminder that fitting the model is never the end of the story.
It’s easy enough to adjust for this in the model by just including an indicator for the dataset. We (that is, Aki) fit a hierarchical Gaussian process to the data, with several additive terms (on the log scale, I assume; at least, it should all be on the log scale). The post Beautiful Graphs for Baseball Strike-Count Performance appeared first on Statistical Modeling, Causal Inference, and Social Science. Jim Albert created some great graphs for strike-count performance in a series of two blog posts.
Albert plots the pooled estimate of expected runs arising at various strike counts for all plate appearances for the 2011 season. Using the x axis for count progression and the y axis for outcome yields a really nice visualization of strike count effects. I like the red line at the average effect for an at-bat (corresponding to a 0-0 count), but I would’ve preferred the actual expected runs on the y axis rather than something standardized to zero.


With multiple graphs, it’d be nice to have the same y axis range and the same ratio of x axis to y axis size (I can never remember how to do this in ggplot).
Of course, the natural next step is to build a hierarchical model to partially pool the ball-strike count effects. I highly recommend clicking through to the original posts if you like baseball; there are many more players illustrated and much more in-depth baseball analysis. The post On deck this week appeared first on Statistical Modeling, Causal Inference, and Social Science. The post Peer review abuse flashback appeared first on Statistical Modeling, Causal Inference, and Social Science.
When any call is made for the retraction of two peer-reviewed and published articles, the onus of proof is on the claimant and the duty of scientific care and caution is manifestly high.
If you think of scientific research as a game where the prizes are publication, renown, and tenured professorships—then yes, by all means, try to bury all criticism and fight so hard that nobody will ever want to mess with you again.
On the other hand, if you think of scientific research as a way to learn about reality, you should be thrilled when your expectations are confounded, when someone points out an error and you have to rethink. Basically, I need to give them some simple answer that is least likely to raise eyebrows when they’re trying to publish. The post Stan talk in Seattle on Tuesday, May 17 appeared first on Statistical Modeling, Causal Inference, and Social Science. The post Leicester City and Donald Trump: How to think about predictions and longshot victories? There’s been a lot of discussion in the sports and political media about what happened here.
The 14th best team in the EPL is roughly equivalent to the 20th best team in MLB or the NBA or the NFL, in terms of distance from the top. There are a bunch of quotes out there from various pundits last year saying that Trump had zero chance of winning to the Republican primary.
In contrast, if I make accurate predictions, ok, fine, I’m supposed to be able to do that. Basically, I applied a bit of auto-peer-review to my own hypothetical blog post on Adams and Trump, and I rejected it!
I think the chance of a Sanders-Trump matchup is so low that we dona€™t have to think too hard about this one! A big difference between EPL and Tyson-Douglas is that there were only two potential winners of the boxing match. Mark Palko points us to this op-ed in which psychiatrist Richard Friedman writes: There are also easy and powerful ways to enhance learning in young people.
The post Happy talk, meet the Edlin factor appeared first on Statistical Modeling, Causal Inference, and Social Science.
At the end of eight weeks, students who had been encouraged to view their intelligence as changeable scored significantly better (85 percent) than controls (54 percent) on a test of the material they learned in the seminar.
Just like the original Jaws 2, this story features neither Richard Dreyfus nor Steven Spielberg. Match the resonses of large nationally representative sample to supporting these policy items. I let this languish in my inbox for awhile until Kahan taunted me by letting me know he’d posted the solution online.
But before I give you our guesses, and before I tell you the actual answers (courtesy of Kahan), give it a try yourself. If you finish early, feel free to click on the random ass pictures that I’ve inserted to prevent you from inadverently spotting the answers before completing the test!
OK, as I said, it’s tricky to do the scoring because Kahan switched some of the colors on us, but if you go back to the top image and compare, you’ll see that we got 8 out of 10 correct! Indeed, if you go with Kahan’s absurdly complicated scoring scheme, we got a perfect score of 14.75! The post Big Belly Roti on Amsterdam Ave and 123 St appeared first on Statistical Modeling, Causal Inference, and Social Science. We may all have our biases, but I’m curious about cutting through those to what useful investigation of the data tells us.A  What does the research behind these say?A  Is that research good, or does it fall prey to low power and that pesky overgrown garden you keep writing about? The topic is interesting in itself and is of particular importance to me because nearly every day I’m thinking about what I can do to keep Stan team members happy and productive, and we do have some mix of structure we can impose and extrinsic rewards we can give out. Finally, the relation between common sense, individual experiences, and statistical evidence is unclear here. Jim Greiner writes: The Access to Justice Lab is a startup effort, initially supported by the Laura and John Arnold Foundation with sufficient funds for three years, headed by Jim Greiner at Harvard Law School. Regular readers will know that Bill James was one of my inspirations for becoming a statistician.


The post Bill James does model checking appeared first on Statistical Modeling, Causal Inference, and Social Science. Total Baseball has Glenn Hubbard rated as a better player than Pete Rose, Brooks Robinson, Dale Murphy, Ken Boyer, or Sandy Koufax, a conclusion which is every bit as preposterous as it seems to be at first blush. To a large extent, this rating is caused by the failure to adjust Hubbard’s fielding statistics for the ground-ball tendency of his pitching staff. Mon: Bill James does model checking Tues: Whata€™s the motivation to do experiments on motivation?
Paul Alper pointed me to this news article about an economist who got BUSTED for doing algebra on the plane.
As a result, the field is being invigorated by initiatives such as study pre-registration and open data. Much as I’d love to think that the famous cartoonist has been leaving lots of comments on my very own blog, ultimately I think the posterior probability is low that fraac is Adams. In that case, the question is mostly just an academic one, because anything we do will come up with more-or-less the same answer. We might be close to the edge of parameter space, or there might be some bimodal situation where two (or more) parameters can combine in two (or more) different plausible ways, and the evidence from the data isn’t yet enough to conclusively rule one of those out.
Now obviously a whole bunch of things have to break right for for a 75-87 team to have the best record in baseball the next year. The bookies arena€™t giving odds on SOME team with a (equivalent to) 75-87 record or worse winning the most games next year a€“ but for one specific team.
Douglasa€™s win was surprising because he was assumed to be completely outclassed by Tyson, and then this stunning thing happened.
Beyond that I think it makes sense to look at a€?precursor dataa€?: near misses and the like. For example, there is intriguing evidence that the attitude that young people have about their own intelligence a€” and what their teachers believe a€” can have a big impact on how well they learn. Dweck and colleagues gave a group of low-achieving seventh graders a seminar on how the brain works and put the students at random into two groups.
It all started when Dan Kahan sent me the following puzzle: Match the resonses of large nationally representative sample to supporting these policy items.
This dude was profiled by the lady sitting next to him who got suspicious of his incomprehensible formulas. Every single belief every person has is a rationalisation of their emotional state a€“ in particular it’s usually hiding from fear. In those cases, plotting the distribution should clearly show that something is up, but what do we do about it in terms of giving a point estimate? 5000-1 may be low, but 50-1 is absurdly high given there actually are quality differences between teams, and there are quite a few of them. Leicester is a pro soccer team and nobody thought they were outclassed by the other teamsa€”on a€?any given Sundaya€? anyone can wina€”but it was thought they were doomed by the law of large numbers. For example if therea€™s not much data on the frequency of longshots winning the championship, we could get data on longshots placing in the top three, or the top five.
Carol Dweck, a psychology professor at Stanford University, has shown that kids who think that their intelligence is malleable perform better and are more motivated to learn than those who believe that their intelligence is fixed and unchangeable. The experimental group was told that learning changes the brain and that students are in charge of this process.
Even the smartest people with the correctest beliefs tell themselves lies that are qualititively identical to those of the craziest loon.
One way to think about Leicestera€™s odds in this context would be to say that, if they really are the 14th-best team, then maybe there are about 10 teams with roughly similar odds as theirs, and one could use historical data to get a sense of whata€™s the probability of any of the bottom 10 teams winning the championship. Therea€™s a continuity to the task of winning the championship, so it should be possible to extrapolate this probability from the probabilities of precursor events. The control group received a lesson on memory, but was not instructed to think of intelligence as malleable. We continue to stand by the analyses, findings and conclusions reported in our earlier publications.
Leicester was the 14th-best team in the league last year in terms of points (and they were better than that in terms of goal differential, which is probably a better indicator of underlying quality). Anyway, the idea that ita€™s a 5000 to 1 shot for the 14th best team in one year to win the league in the next is obviously absurd on its face.




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