andrew_gelman_stats andrew_gelman_stats-2014 andrew_gelman_stats-2014-2362 knowledge-graph by maker-knowledge-mining
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Introduction: Roy Mendelssohn points me to this excellent bit of statistics reporting by Matt Novak. I have no comment, I just think it’s good to see this sort of high-quality Felix Salmon-style statistically savvy journalism.
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2 I have no comment, I just think it’s good to see this sort of high-quality Felix Salmon-style statistically savvy journalism. [sent-2, score-0.853]
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same-blog 1 1.0 2362 andrew gelman stats-2014-06-06-Statistically savvy journalism
Introduction: Roy Mendelssohn points me to this excellent bit of statistics reporting by Matt Novak. I have no comment, I just think it’s good to see this sort of high-quality Felix Salmon-style statistically savvy journalism.
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Introduction: Alan and Felix .
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Introduction: Roy Mendelssohn pointed me to this heartwarming story of Jay Vadiveloo, an actuary who got a patent for the idea of statistical sampling. Vadiveloo writes, “the results were astounding: statistical sampling worked.” You may laugh, but wait till Albedo Man buys the patent and makes everybody do his bidding. They’re gonna dig up Laplace and make him pay retroactive royalties. And somehow Clippy will get involved in all this. P.S. Mendelssohn writes: “Yes, I felt it was a heartwarming story also. Perhaps we can get a patent for regression.” I say, forget a patent for regression. I want a patent for the sample mean. That’s where the real money is. You can’t charge a lot for each use, but consider the volume!
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Introduction: A link from Simon Jackman’s blog led me to an article by James Heckman, Hedibert Lopes, and Remi Piatek from 2011, “Treatment effects: A Bayesian perspective.” I was pleasantly surprised to see this, partly because I didn’t know that Heckman was working on Bayesian methods, and partly because the paper explicitly refers to the “potential outcomes model,” a term I associate with Don Rubin. I’ve had the impression that Heckman and Rubin don’t like each other (I was a student of Rubin and have never met Heckman, so I’m only speaking at second hand here), so I was happy to see some convergence. I was curious how Heckman et al. would source the potential outcome model. They do not refer to Rubin’s 1974 paper or to Neyman’s 1923 paper (which was republished in 1990 and is now taken to be the founding document of the Neyman-Rubin approach to causal inference). Nor, for that matter, do Heckman et al. refer to the more recent developments of these theories by Robins, Pearl, and other
Introduction: The official announcement: The Excellence in Statistical Reporting Award for 2010 is presented to Felix Salmon for his body of work, which exemplifies the highest standards of scientific reporting. His insightful use of statistics as a tool to understanding the world of business and economics, areas that are critical in today’s economy, sets a new standard in statistical investigative reporting. Here are some examples: Tiger Woods Nigerian spammers How the government fudges job statistics This one is important to me. The idea is that “statistical reporting” is not just traditional science reporting (journalist talks with scientists and tries to understand the consensus) or science popularization or silly feature stories about the lottery. Salmon is doing investigative reporting using statistical thinking. Also, from a political angle, Salmon’s smart and quantitatively sophisticated work (as well as that of others such as Nate Silver) is an important counterweigh
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Introduction: Roy Mendelssohn points me to this excellent bit of statistics reporting by Matt Novak. I have no comment, I just think it’s good to see this sort of high-quality Felix Salmon-style statistically savvy journalism.
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Introduction: The title of this post by Sanjay Srivastava illustrates an annoying misconception that’s crept into the (otherwise delightful) recent publicity related to my article with Hal Stern, he difference between “significant” and “not significant” is not itself statistically significant. When people bring this up, they keep referring to the difference between p=0.05 and p=0.06, making the familiar (and correct) point about the arbitrariness of the conventional p-value threshold of 0.05. And, sure, I agree with this, but everybody knows that already. The point Hal and I were making was that even apparently large differences in p-values are not statistically significant. For example, if you have one study with z=2.5 (almost significant at the 1% level!) and another with z=1 (not statistically significant at all, only 1 se from zero!), then their difference has a z of about 1 (again, not statistically significant at all). So it’s not just a comparison of 0.05 vs. 0.06, even a differenc
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Introduction: If an estimate is statistically significant, it’s probably an overestimate of the magnitude of your effect. P.S. I think youall know what I mean here. But could someone rephrase it in a more pithy manner? I’d like to include it in our statistical lexicon.
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Introduction: “Ethics and Statistics” is descriptive but boring. It sounds like the textbook for a course which, unfortunately, nobody will take. “Lies, Damn Lies, and Statistics” is too unoriginal. “How to Lie, Cheat, and Steal With Statistics” is kind of ok, maybe? “Statistical Dilemmas”: maybe a bit too boring as well. “Knaves and Frauds of Statistics, and Some Guys Who’ve Skated a Bit Close to the Edge”: Hmmm…. Maybe we have to get “statistics” out of the title altogether? “Knaves and Frauds of Data Science”? “Date Science and Data Fraud”? “10 Things You Really Really Really Shouldn’t Do With Numbers”? And, if no better idea comes along, there’s always “Evilicious: Why We Evolved a Taste for Being Bad.” (Regular readers will know what I’m talking about here; the rest of you can google it.) Or maybe just “The Wegman Report”? It’s hard to come up with a good title. Even John Updike had difficulties in this regard. If any of you can suggest a better title for my eth
Introduction: The official announcement: The Excellence in Statistical Reporting Award for 2010 is presented to Felix Salmon for his body of work, which exemplifies the highest standards of scientific reporting. His insightful use of statistics as a tool to understanding the world of business and economics, areas that are critical in today’s economy, sets a new standard in statistical investigative reporting. Here are some examples: Tiger Woods Nigerian spammers How the government fudges job statistics This one is important to me. The idea is that “statistical reporting” is not just traditional science reporting (journalist talks with scientists and tries to understand the consensus) or science popularization or silly feature stories about the lottery. Salmon is doing investigative reporting using statistical thinking. Also, from a political angle, Salmon’s smart and quantitatively sophisticated work (as well as that of others such as Nate Silver) is an important counterweigh
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Introduction: Roy Mendelssohn points me to this excellent bit of statistics reporting by Matt Novak. I have no comment, I just think it’s good to see this sort of high-quality Felix Salmon-style statistically savvy journalism.
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Introduction: Megan Price wrote in that she and Daniel Guzmán of the Benetech Human Rights Program released a paper today entitled “Comments to the article ‘Is Violence Against Union Members in Colombia Systematic and Targeted?’” (o aqui en español), which examines an article written by Colombian academics Daniel Mejía and María José Uribe. Price writes [in the third person]: The paper reviewed by Price and Guzmán concluded that “. . . on average, violence against unionists in Colombia is neither systematic nor targeted.” However, in their response, Price and Guzmán present – in technical and methodological detail – the reasons they find the conclusions in Mejía and Uribe’s study to be overstated. Price and Guzmán believe that weaknesses in the data, in the choice of the statistical model, and the interpretation of the model used in Mejía and Uribe’s study, all raise serious questions about the authors’ strong causal conclusions. Price and Guzmán point out that unchecked, those conclusio
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Introduction: Pete Gries writes: I [Gries] am not sure if what you are suggesting by “doing data analysis in a patternless way” is a pitch for deductive over inductive approaches as a solution to the problem of reporting and publication bias. If so, I may somewhat disagree. A constant quest to prove or disprove theory in a deductive manner is one of the primary causes of both reporting and publication bias. I’m actually becoming a proponent of a remarkably non-existent species – “applied political science” – because there is so much animosity in our discipline to inductive empirical statistical work that seeks to answer real world empirical questions rather than contribute to parsimonious theory building. Anyone want to start a JAPS – Journal of Applied Political Science? Our discipline is in danger of irrelevance. My reply: By “doing data analysis in a patternless way,” I meant statistical methods such as least squares, maximum likelihood, etc., that estimate parameters independently witho
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Introduction: Roy Mendelssohn pointed me to this heartwarming story of Jay Vadiveloo, an actuary who got a patent for the idea of statistical sampling. Vadiveloo writes, “the results were astounding: statistical sampling worked.” You may laugh, but wait till Albedo Man buys the patent and makes everybody do his bidding. They’re gonna dig up Laplace and make him pay retroactive royalties. And somehow Clippy will get involved in all this. P.S. Mendelssohn writes: “Yes, I felt it was a heartwarming story also. Perhaps we can get a patent for regression.” I say, forget a patent for regression. I want a patent for the sample mean. That’s where the real money is. You can’t charge a lot for each use, but consider the volume!
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Introduction: Trey Causey writes: Do you have suggestions as to model selection strategies akin to Bayesian model averaging for multilevel models when level-2 inputs are of substantive interest? I [Causey] have seen plenty of R packages and procedures for non-multilevel models, and tried the glmulti package but found that it did not perform well with more than a few level-2 variables. My quick answer is: with a name like that, you should really be fitting three-level models! My longer answer is: regular readers will be unsurprised to hear that I’m no fan of Bayesian model averaging . Instead I’d prefer to bite the bullet and assign an informative prior distribution on these coefficients. I don’t have a great example of such an analysis but I’m more and more thinking that this is the way to go. I don’t see the point in aiming for the intermediate goal of pruning the predictors; I’d rather have a procedure that includes prior information on the predictors and their interactions.
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