andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-2001 knowledge-graph by maker-knowledge-mining
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Introduction: Antony Unwin writes: Rereading Edgar Allan Poe’s “Murder in the Rue Morgue” reminded me of his astute remarks on analysis. For instance But it is in matters beyond the limits of mere rule that the skill of the analyst is evinced. He makes, in silence, a host of observations and inferences. and and the difference in the extent of the information obtained, lies not so much in the validity of the inference as in the quality of the observation. The necessary knowledge is that of what to observe. and He impaired his vision by holding the object too close. He might see, perhaps, one or two points with unusual clearness, but in so doing he, necessarily, lost sight of the matter as a whole. However, I had forgotten his following comment, which rang all sorts of bells in connection with some scientific articles I have seen recently: what is only complex is mistaken (a not unusual error) for what is profound. How about asking referees to rate articles on their complex
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1 Antony Unwin writes: Rereading Edgar Allan Poe’s “Murder in the Rue Morgue” reminded me of his astute remarks on analysis. [sent-1, score-0.418]
2 For instance But it is in matters beyond the limits of mere rule that the skill of the analyst is evinced. [sent-2, score-0.968]
3 He makes, in silence, a host of observations and inferences. [sent-3, score-0.263]
4 and and the difference in the extent of the information obtained, lies not so much in the validity of the inference as in the quality of the observation. [sent-4, score-0.45]
5 The necessary knowledge is that of what to observe. [sent-5, score-0.184]
6 and He impaired his vision by holding the object too close. [sent-6, score-0.612]
7 He might see, perhaps, one or two points with unusual clearness, but in so doing he, necessarily, lost sight of the matter as a whole. [sent-7, score-0.616]
8 However, I had forgotten his following comment, which rang all sorts of bells in connection with some scientific articles I have seen recently: what is only complex is mistaken (a not unusual error) for what is profound. [sent-8, score-1.427]
9 How about asking referees to rate articles on their complexity and their profundity? [sent-9, score-0.599]
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same-blog 1 1.0000001 2001 andrew gelman stats-2013-08-29-Edgar Allan Poe was a statistician
Introduction: Antony Unwin writes: Rereading Edgar Allan Poe’s “Murder in the Rue Morgue” reminded me of his astute remarks on analysis. For instance But it is in matters beyond the limits of mere rule that the skill of the analyst is evinced. He makes, in silence, a host of observations and inferences. and and the difference in the extent of the information obtained, lies not so much in the validity of the inference as in the quality of the observation. The necessary knowledge is that of what to observe. and He impaired his vision by holding the object too close. He might see, perhaps, one or two points with unusual clearness, but in so doing he, necessarily, lost sight of the matter as a whole. However, I had forgotten his following comment, which rang all sorts of bells in connection with some scientific articles I have seen recently: what is only complex is mistaken (a not unusual error) for what is profound. How about asking referees to rate articles on their complex
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Introduction: I like what Antony Unwin has to say here (start on page 5).
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Introduction: Antony Unwin writes: I [Unwin] find it an interesting exercise for students to ask them to write headlines (and subheadlines) for graphics, both for ones they have drawn themselves and for published ones. The results are sometimes depressing, often thought-provoking and occasionally highly entertaining. This seems like a great idea, both for teaching students how to read a graph and also for teaching how to make a graph. I’ve long said that when making a graph (or, for that matter, a table), you want to think about what message the reader will get out of it. “Displaying a bunch of numbers” doesn’t cut it.
4 0.086790189 816 andrew gelman stats-2011-07-22-“Information visualization” vs. “Statistical graphics”
Introduction: By now you all must be tired of my one-sided presentations of the differences between infovis and statgraphics (for example, this article with Antony Unwin). Today is something different. Courtesy of Martin Theus, editor of the Statistical Computing and Graphics Newsletter, we have two short articles offering competing perspectives: Robert Kosara writes from an Infovis view: Information visualization is a field that has had trouble defining its boundaries, and that consequently is often misunderstood. It doesn’t help that InfoVis, as it is also known, produces pretty pictures that people like to look at and link to or send around. But InfoVis is more than pretty pictures, and it is more than statistical graphics. The key to understanding InfoVis is to ignore the images for a moment and focus on the part that is often lost: interaction. When we use visualization tools, we don’t just create one image or one kind of visualization. In fact, most people would argue that there is
5 0.082605153 822 andrew gelman stats-2011-07-26-Any good articles on the use of error bars?
Introduction: Hadley Wickham asks: I was wondering if you knew of any good articles on the use of error bars. I’m particularly looking for articles that discuss the difference between error of means and error of difference in the context of models (e.g. mixed models) where they are very different. I suspect every applied field has a couple of good articles, but it’s really hard to search for them. Can anyone help on this? My only advice is to get rid of those horrible crossbars at the ends of the error bars. The crossbars draw attention to the error bars’ endpoints, which are generally not important at all. See, for example, my Anova paper , for some examples of how I like error bars to look.
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Introduction: Antony Unwin writes: Rereading Edgar Allan Poe’s “Murder in the Rue Morgue” reminded me of his astute remarks on analysis. For instance But it is in matters beyond the limits of mere rule that the skill of the analyst is evinced. He makes, in silence, a host of observations and inferences. and and the difference in the extent of the information obtained, lies not so much in the validity of the inference as in the quality of the observation. The necessary knowledge is that of what to observe. and He impaired his vision by holding the object too close. He might see, perhaps, one or two points with unusual clearness, but in so doing he, necessarily, lost sight of the matter as a whole. However, I had forgotten his following comment, which rang all sorts of bells in connection with some scientific articles I have seen recently: what is only complex is mistaken (a not unusual error) for what is profound. How about asking referees to rate articles on their complex
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Introduction: Following my talk on infovis and statistical graphics at the Empirical Legal Studies conference , Dan Kahan writes: The legal academy, which is making strides toward sensible integration of a variety of empirical methods into its scholarship, is horribly ignorant of the utility of graphic reporting of data, a likely influence of the formative influence that econometric methods has exerted on expectations and habits of mind among legal scholars. Lee Epstein has written a pair of wonderful articles on graphic reporting – 1. Epstein, L., Martin, A. & Boyd, C. On the Effective Communication of the Results of Empirical Studies, Part II. Vand. L. Rev. 60, 798-846 (2007). 2. Epstein, L., Martin, A. & Schneider, M. On the Effective Communication of the Results of Empirical Studies, Part I. Vand. L. Rev. 59, 1811-1871 (2007). – but her efforts haven’t gotten the attention they deserve, and reinforcement, particularly at a venue like CELS is very important. But the main issue there
Introduction: In “Story: A Definition,” visual analysis researcher Robert Kosara writes : A story ties facts together. There is a reason why this particular collection of facts is in this story, and the story gives you that reason. provides a narrative path through those facts. In other words, it guides the viewer/reader through the world, rather than just throwing them in there. presents a particular interpretation of those facts. A story is always a particular path through a world, so it favors one way of seeing things over all others. The relevance of these ideas to statistical graphics is apparent. From a completely different direction, in “When do stories work? Evidence and illustration in the social sciences,” Thomas Basbøll and I write : Storytelling has long been recognized as central to human cognition and communication. Here we explore a more active role of stories in social science research, not merely to illustrate concepts but also to develop new ideas and evalu
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Introduction: Dan Lakeland asks : When are statistical graphics potentially life threatening? When they’re poorly designed, and used to make decisions on potentially life threatening topics, like medical decision making, engineering design, and the like. The American Academy of Pediatrics has dropped the ball on communicating to physicians about infant jaundice. Another message in this post is that bad decisions can compound each other. It’s an interesting story (follow the link above for the details), would be great for a class in decision analysis or statistical communication. I have no idea how to get from A to B here, in the sense of persuading hospitals to do this sort of thing better. I’d guess the first step is to carefully lay out costs and benefits. When doctors and nurses make extra precautions for safety, it could be useful to lay out the ultimate goals and estimate the potential costs and benefits of different approaches.
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Introduction: Over the years I’ve written a dozen or so journal articles that have appeared with discussions, and I’ve participated in many published discussions of others’ articles as well. I get a lot out of these article-discussion-rejoinder packages, in all three of my roles as reader, writer, and discussant. Part 1: The story of an unsuccessful discussion The first time I had a discussion article was the result of an unfortunate circumstance. I had a research idea that resulted in an article with Don Rubin on monitoring the mixing of Markov chain simulations. I new the idea was great, but back then we worked pretty slowly so it was awhile before we had a final version to submit to a journal. (In retrospect I wish I’d just submitted the draft version as it was.) In the meantime I presented the paper at a conference. Our idea was very well received (I had a sheet of paper so people could write their names and addresses to get preprints, and we got either 50 or 150 (I can’t remembe
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Introduction: Antony Unwin writes: Rereading Edgar Allan Poe’s “Murder in the Rue Morgue” reminded me of his astute remarks on analysis. For instance But it is in matters beyond the limits of mere rule that the skill of the analyst is evinced. He makes, in silence, a host of observations and inferences. and and the difference in the extent of the information obtained, lies not so much in the validity of the inference as in the quality of the observation. The necessary knowledge is that of what to observe. and He impaired his vision by holding the object too close. He might see, perhaps, one or two points with unusual clearness, but in so doing he, necessarily, lost sight of the matter as a whole. However, I had forgotten his following comment, which rang all sorts of bells in connection with some scientific articles I have seen recently: what is only complex is mistaken (a not unusual error) for what is profound. How about asking referees to rate articles on their complex
2 0.76943505 448 andrew gelman stats-2010-12-03-This is a footnote in one of my papers
Introduction: In the annals of hack literature, it is sometimes said that if you aim to write best-selling crap, all you’ll end up with is crap. To truly produce best-selling crap, you have to have a conviction, perhaps misplaced, that your writing has integrity. Whether or not this is a good generalization about writing, I have seen an analogous phenomenon in statistics: If you try to do nothing but model the data, you can be in for a wild and unpleasant ride: real data always seem to have one more twist beyond our ability to model (von Neumann’s elephant’s trunk notwithstanding). But if you model the underlying process, sometimes your model can fit surprisingly well as well as inviting openings for future research progress.
3 0.75513923 1037 andrew gelman stats-2011-12-01-Lamentably common misunderstanding of meritocracy
Introduction: Tyler Cowen pointed to an article by business-school professor Luigi Zingales about meritocracy. I’d expect a b-school prof to support the idea of meritocracy, and Zingales does not disappoint. But he says a bunch of other things that to me represent a confused conflation of ideas. Here’s Zingales: America became known as a land of opportunity—a place whose capitalist system benefited the hardworking and the virtuous [emphasis added]. In a word, it was a meritocracy. That’s interesting—and revealing. Here’s what I get when I look up “meritocracy” in the dictionary : 1 : a system in which the talented are chosen and moved ahead on the basis of their achievement 2 : leadership selected on the basis of intellectual criteria Nothing here about “hardworking” or “virtuous.” In a meritocracy, you can be as hardworking as John Kruk or as virtuous as Kobe Bryant and you’ll still get ahead—if you have the talent and achievement. Throwing in “hardworking” and “virtuous”
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Introduction: As we get more data, we can fit more model. But at some point we become so overwhelmed by data that, for computational reasons, we can barely do anything at all. Thus, the curve above could be thought of as the product of two curves: a steadily increasing curve showing the statistical ability to fit more complex models with more data, and a steadily decreasing curve showing the computational feasibility of doing so.
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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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