andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-2013 knowledge-graph by maker-knowledge-mining
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Introduction: Under the heading, “Bad graph candidate,” Kevin Wright points to this article [link fixed], writing: Some of the figures use the same line type for two different series. More egregious are the confidence intervals that are constant width instead of increasing in width into the future. Indeed. What’s even more embarrassing is that these graphs appeared in an article in the magazine Significance, sponsored by the American Statistical Association and the Royal Statistical Society. Perhaps every scientific journal could have a graphics editor whose job is to point out really horrible problems and require authors to make improvements. The difficulty, as always, is that scientists write these articles for free and as a public service (publishing in Significance doesn’t pay, nor does it count as a publication in an academic record), so it might be difficult to get authors to fix their graphs. On the other hand, if an article is worth writing at all, it’s worth trying to conv
sentIndex sentText sentNum sentScore
1 Under the heading, “Bad graph candidate,” Kevin Wright points to this article [link fixed], writing: Some of the figures use the same line type for two different series. [sent-1, score-0.213]
2 More egregious are the confidence intervals that are constant width instead of increasing in width into the future. [sent-2, score-1.152]
3 What’s even more embarrassing is that these graphs appeared in an article in the magazine Significance, sponsored by the American Statistical Association and the Royal Statistical Society. [sent-4, score-0.763]
4 Perhaps every scientific journal could have a graphics editor whose job is to point out really horrible problems and require authors to make improvements. [sent-5, score-0.595]
5 The difficulty, as always, is that scientists write these articles for free and as a public service (publishing in Significance doesn’t pay, nor does it count as a publication in an academic record), so it might be difficult to get authors to fix their graphs. [sent-6, score-0.793]
6 On the other hand, if an article is worth writing at all, it’s worth trying to convey conclusions clearly. [sent-7, score-0.706]
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same-blog 1 0.99999994 2013 andrew gelman stats-2013-09-08-What we need here is some peer review for statistical graphics
Introduction: Under the heading, “Bad graph candidate,” Kevin Wright points to this article [link fixed], writing: Some of the figures use the same line type for two different series. More egregious are the confidence intervals that are constant width instead of increasing in width into the future. Indeed. What’s even more embarrassing is that these graphs appeared in an article in the magazine Significance, sponsored by the American Statistical Association and the Royal Statistical Society. Perhaps every scientific journal could have a graphics editor whose job is to point out really horrible problems and require authors to make improvements. The difficulty, as always, is that scientists write these articles for free and as a public service (publishing in Significance doesn’t pay, nor does it count as a publication in an academic record), so it might be difficult to get authors to fix their graphs. On the other hand, if an article is worth writing at all, it’s worth trying to conv
2 0.16529852 2245 andrew gelman stats-2014-03-12-More on publishing in journals
Introduction: I’m postponing today’s scheduled post (“Empirical implications of Empirical Implications of Theoretical Models”) to continue the lively discussion from yesterday, What if I were to stop publishing in journals? . An example: my papers with Basbøll Thomas Basbøll and I got into a long discussion on our blogs about business school professor Karl Weick and other cases of plagiarism copying text without attribution. We felt it useful to take our ideas to the next level and write them up as a manuscript, which ended up being logical to split into two papers. At that point I put some effort into getting these papers published, which I eventually did: To throw away data: Plagiarism as a statistical crime went into American Scientist and When do stories work? Evidence and illustration in the social sciences will appear in Sociological Methods and Research. The second paper, in particular, took some effort to place; I got some advice from colleagues in sociology as to where
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Introduction: Good stuff.
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Introduction: For background, you can start by reading my recent article, Is It Possible to Be an Ethicist Without Being Mean to People? and then a blog post, Quality over Quantity , by John Cook, who writes: At one point [Ed] Tufte spoke more generally and more personally about pursuing quality over quantity. He said most papers are not worth reading and that he learned early on to concentrate on the great papers, maybe one in 500, that are worth reading and rereading rather than trying to “keep up with the literature.” He also explained how over time he has concentrated more on showcasing excellent work than on criticizing bad work. You can see this in the progression from his first book to his latest. (Criticizing bad work is important too, but you’ll have to read his early books to find more of that. He won’t spend as much time talking about it in his course.) That reminded me of Jesse Robbins’ line: “Don’t fight stupid. You are better than that. Make more awesome.” This made me stop an
Introduction: The other day we discussed that paper on ovulation and voting (you may recall that the authors reported a scattered bunch of comparisons, significance tests, and p-values, and I recommended that they would’ve done better to simply report complete summaries of their data, so that readers could see the comparisons of interest in full context), and I was thinking a bit more about why I was so bothered that it was published in Psychological Science, which I’d thought of as a serious research journal. My concern isn’t just that that the paper is bad—after all, lots of bad papers get published—but rather that it had nothing really going for it, except that it was headline bait. It was a survey done on Mechanical Turk, that’s it. No clever design, no clever questions, no care in dealing with nonresponse problems, no innovative data analysis, no nothing. The paper had nothing to offer, except that it had no obvious flaws. Psychology is a huge field full of brilliant researchers.
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Introduction: Under the heading, “Bad graph candidate,” Kevin Wright points to this article [link fixed], writing: Some of the figures use the same line type for two different series. More egregious are the confidence intervals that are constant width instead of increasing in width into the future. Indeed. What’s even more embarrassing is that these graphs appeared in an article in the magazine Significance, sponsored by the American Statistical Association and the Royal Statistical Society. Perhaps every scientific journal could have a graphics editor whose job is to point out really horrible problems and require authors to make improvements. The difficulty, as always, is that scientists write these articles for free and as a public service (publishing in Significance doesn’t pay, nor does it count as a publication in an academic record), so it might be difficult to get authors to fix their graphs. On the other hand, if an article is worth writing at all, it’s worth trying to conv
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Introduction: In his review in 1938 of Historical Development of the Graphical Representation of Statistical Data , by H. Gray Funkhauser, for The Economic Journal , the great economist writes: Perhaps the most striking outcome of Mr. Funkhouser’s researches is the fact of the very slow progress which graphical methods made until quite recently. . . . In the first fifty volumes of the Statistical Journal, 1837-87, only fourteen graphs are printed altogether. It is surprising to be told that Laplace never drew a graph of the normal law of error . . . Edgeworth made no use of statistical charts as distinct from mathematical diagrams. Apart from Quetelet and Jevons, the most important influences were probably those of Galton and of Mulhall’s Dictionary, first published in 1884. Galton was indeed following his father and grandfather in this field, but his pioneer work was mainly restricted to meteorological maps, and he did not contribute to the development of the graphical representation of ec
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Introduction: Stephen Jenkins wrote: I was thinking that you and your blog readers might be interested in “ An Economist’s Guide to Visualizing Data ” by Jonathan Schwabish, in the most recent Journal of Economic Perspectives (which is the American Economic Association’s main “outreach” journal in some ways). I replied: Ooh, I hate this so much! This seems to represent a horrible example of economists not recognizing that outsiders can help them. We do much much better in political science. To which Jenkins wrote: Ha! I guessed as much — hence sent it. And I’ll now admit I was surprised that JEP took the piece without getting Schwabisch to widen his reference points. To elaborate a bit: I agree with Schwabish’s general advice (“show the data,” “reduce the clutter,” and “integrate the text and the graph”). But then he illustrates with 8 before-and-after stories in which he shows an existing graph and then gives his improvements. My problem is that I don’t like most of his “afte
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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
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Introduction: Thomas Basbøll pointed me to a discussion on the orgtheory blog in which Jerry Davis, the editor of a journal of business management argued that it is difficult for academic researchers to communicate with the public because “the public prefers Cheetos to a healthy salad” and when serious papers are discussed on the internet, “everyone is a methodologist.” The discussion heated up when an actual methodologist, Steve Morgan, joined in to argue that the salad in question was not so healthy and that the much-derided internet commenters made some valuable points. The final twist was that one of the orgtheory bloggers deleted a comment and then closed the thread entirely when the discussion got too conflictual. In a few days I’ll return to the meta-topic of the discussion, but right now I want to focus on one thing Davis wrote, a particular statement that illustrates to me the gap between the rhetorical and the literal, the way in which a statement can sound good but make no sense. He
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same-blog 1 0.97153151 2013 andrew gelman stats-2013-09-08-What we need here is some peer review for statistical graphics
Introduction: Under the heading, “Bad graph candidate,” Kevin Wright points to this article [link fixed], writing: Some of the figures use the same line type for two different series. More egregious are the confidence intervals that are constant width instead of increasing in width into the future. Indeed. What’s even more embarrassing is that these graphs appeared in an article in the magazine Significance, sponsored by the American Statistical Association and the Royal Statistical Society. Perhaps every scientific journal could have a graphics editor whose job is to point out really horrible problems and require authors to make improvements. The difficulty, as always, is that scientists write these articles for free and as a public service (publishing in Significance doesn’t pay, nor does it count as a publication in an academic record), so it might be difficult to get authors to fix their graphs. On the other hand, if an article is worth writing at all, it’s worth trying to conv
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Introduction: Stephen Jenkins wrote: I was thinking that you and your blog readers might be interested in “ An Economist’s Guide to Visualizing Data ” by Jonathan Schwabish, in the most recent Journal of Economic Perspectives (which is the American Economic Association’s main “outreach” journal in some ways). I replied: Ooh, I hate this so much! This seems to represent a horrible example of economists not recognizing that outsiders can help them. We do much much better in political science. To which Jenkins wrote: Ha! I guessed as much — hence sent it. And I’ll now admit I was surprised that JEP took the piece without getting Schwabisch to widen his reference points. To elaborate a bit: I agree with Schwabish’s general advice (“show the data,” “reduce the clutter,” and “integrate the text and the graph”). But then he illustrates with 8 before-and-after stories in which he shows an existing graph and then gives his improvements. My problem is that I don’t like most of his “afte
Introduction: Sandeep Baliga writes : [In a recent study , Gilles Duranton and Matthew Turner write:] For interstate highways in metropolitan areas we [Duranton and Turner] find that VKT (vehicle kilometers traveled) increases one for one with interstate highways, confirming the fundamental law of highway congestion.’ Provision of public transit also simply leads to the people taking public transport being replaced by drivers on the road. Therefore: These findings suggest that both road capacity expansions and extensions to public transit are not appropriate policies with which to combat traffic congestion. This leaves congestion pricing as the main candidate tool to curb traffic congestion. To which I reply: Sure, if your goal is to curb traffic congestion . But what sort of goal is that? Thinking like a microeconomist, my policy goal is to increase people’s utility. Sure, traffic congestion is annoying, but there must be some advantages to driving on that crowded road or pe
4 0.95153427 248 andrew gelman stats-2010-09-01-Ratios where the numerator and denominator both change signs
Introduction: A couple years ago, I used a question by Benjamin Kay as an excuse to write that it’s usually a bad idea to study a ratio whose denominator has uncertain sign. As I wrote then: Similar problems arise with marginal cost-benefit ratios, LD50 in logistic regression (see chapter 3 of Bayesian Data Analysis for an example), instrumental variables, and the Fieller-Creasy problem in theoretical statistics. . . . In general, the story is that the ratio completely changes in interpretation when the denominator changes sign. More recently, Kay sent in a related question: I [Kay] wondered if you have any advice on handling ratios when the signs change as a result of a parameter. I have three functions, one C * x^a, another D * x^a, and a third f(x,a) in my paper such that: C * x^a, < f(x,a) < D * x^a C,D and a all have the same signs. We can divide through by C * x^a but the results depend on the sign of C either 1< f(x,a) / C * x^a < D * x^a / C * x^a, or 1 / f(x,a
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