andrew_gelman_stats andrew_gelman_stats-2012 andrew_gelman_stats-2012-1604 knowledge-graph by maker-knowledge-mining
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Introduction: Here . Indeed, I’d much rather be a legend than a myth. I just want to clarify one thing. Walter Hickey writes: [Antony Unwin and Andrew Gelman] collaborated on this presentation where they take a hard look at what’s wrong with the recent trends of data visualization and infographics. The takeaway is that while there have been great leaps in visualization technology, some of the visualizations that have garnered the highest praises have actually been lacking in a number of key areas. Specifically, the pair does a takedown of the top visualizations of 2008 as decided by the popular statistics blog Flowing Data. This is a fair summary, but I want to emphasize that, although our dislike of some award-winning visualizations is central to our argument, it is only the first part of our story. As Antony and I worked more on our paper, and especially after seeing the discussions by Robert Kosara, Stephen Few, Hadley Wickham, and Paul Murrell (all to appear in Journal of Computati
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1 Walter Hickey writes: [Antony Unwin and Andrew Gelman] collaborated on this presentation where they take a hard look at what’s wrong with the recent trends of data visualization and infographics. [sent-4, score-0.217]
2 The takeaway is that while there have been great leaps in visualization technology, some of the visualizations that have garnered the highest praises have actually been lacking in a number of key areas. [sent-5, score-0.816]
3 Specifically, the pair does a takedown of the top visualizations of 2008 as decided by the popular statistics blog Flowing Data. [sent-6, score-0.677]
4 This is a fair summary, but I want to emphasize that, although our dislike of some award-winning visualizations is central to our argument, it is only the first part of our story. [sent-7, score-0.453]
5 Some of the images that Antony and I don’t like as statistical graphics are fine as visualizations —they are unique, intriguing, and invite the viewer to learn more. [sent-9, score-0.897]
6 Statistical graphs are different: they provide a more direct mapping of data but are better suited to viewers who are already interested in the topic. [sent-10, score-0.388]
7 In the internet age, we should not have to choose between attractive graphs and informational graphs: it should be possible to display both, via interactive displays. [sent-11, score-0.48]
8 But to follow this suggestion, one must first accept that not every beautiful graph is informative, and not every informative graph is beautiful. [sent-12, score-0.675]
9 Yes, it can sometimes be possible for a graph to be both beautiful and informative, as in Minard’s famous Napoleon-in-Russia map, or more recently the Baby Name Wizard, which we featured in our article. [sent-13, score-0.361]
10 But such synergy is not always possible, and we believe that an approach to data graphics that focuses on celebrating such wonderful examples can mislead people by obscuring the tradeoffs between the goals of visual appeal to outsiders and statistical communication to experts. [sent-14, score-1.099]
11 Thus, in pointing out problems with the visualizations that were celebrated on the Flowing Data blog, we are not meaning to do a “takedown” of those images but rather to use these examples to explore the different goals of information graphics. [sent-15, score-0.793]
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same-blog 1 0.99999976 1604 andrew gelman stats-2012-12-04-An epithet I can live with
Introduction: Here . Indeed, I’d much rather be a legend than a myth. I just want to clarify one thing. Walter Hickey writes: [Antony Unwin and Andrew Gelman] collaborated on this presentation where they take a hard look at what’s wrong with the recent trends of data visualization and infographics. The takeaway is that while there have been great leaps in visualization technology, some of the visualizations that have garnered the highest praises have actually been lacking in a number of key areas. Specifically, the pair does a takedown of the top visualizations of 2008 as decided by the popular statistics blog Flowing Data. This is a fair summary, but I want to emphasize that, although our dislike of some award-winning visualizations is central to our argument, it is only the first part of our story. As Antony and I worked more on our paper, and especially after seeing the discussions by Robert Kosara, Stephen Few, Hadley Wickham, and Paul Murrell (all to appear in Journal of Computati
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Introduction: The visual display of quantitative information (to use Edward Tufte’s wonderful term) is a diverse field or set of fields, and its practitioners have different goals. The goals of software designers, applied statisticians, biologists, graphic designers, and journalists (to list just a few of the important creators of data graphics) often overlap—but not completely. One of our aims in writing our article [on Infovis and Statistical Graphics] was to emphasize the diversity of graphical goals, as it seems to us that even experts tend to consider one aspect of a graph and not others. Our main practical suggestion was that, in the internet age, we should not have to choose between attractive graphs and informational graphs: it should be possible to display both, via interactive displays. But to follow this suggestion, one must first accept that not every beautiful graph is informative, and not every informative graph is beautiful. . . . Yes, it can sometimes be possible for a graph to
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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
Introduction: Our discussion on data visualization continues. One one side are three statisticians–Antony Unwin, Kaiser Fung, and myself. We have been writing about the different goals served by information visualization and statistical graphics. On the other side are graphics experts (sorry for the imprecision, I don’t know exactly what these people do in their day jobs or how they are trained, and I don’t want to mislabel them) such as Robert Kosara and Jen Lowe , who seem a bit annoyed at how my colleagues and myself seem to follow the Tufte strategy of criticizing what we don’t understand. And on the third side are many (most?) academic statisticians, econometricians, etc., who don’t understand or respect graphs and seem to think of visualization as a toy that is unrelated to serious science or statistics. I’m not so interested in the third group right now–I tried to communicate with them in my big articles from 2003 and 2004 )–but I am concerned that our dialogue with the graphic
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Introduction: To continue our discussion from last week , consider three positions regarding the display of information: (a) The traditional tabular approach. This is how most statisticians, econometricians, political scientists, sociologists, etc., seem to operate. They understand the appeal of a pretty graph, and they’re willing to plot some data as part of an exploratory data analysis, but they see their serious research as leading to numerical estimates, p-values, tables of numbers. These people might use a graph to illustrate their points but they don’t see them as necessary in their research. (b) Statistical graphics as performed by Howard Wainer, Bill Cleveland, Dianne Cook, etc. They–we–see graphics as central to the process of statistical modeling and data analysis and are interested in graphs (static and dynamic) that display every data point as transparently as possible. (c) Information visualization or infographics, as performed by graphics designers and statisticians who are
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Introduction: Here . Indeed, I’d much rather be a legend than a myth. I just want to clarify one thing. Walter Hickey writes: [Antony Unwin and Andrew Gelman] collaborated on this presentation where they take a hard look at what’s wrong with the recent trends of data visualization and infographics. The takeaway is that while there have been great leaps in visualization technology, some of the visualizations that have garnered the highest praises have actually been lacking in a number of key areas. Specifically, the pair does a takedown of the top visualizations of 2008 as decided by the popular statistics blog Flowing Data. This is a fair summary, but I want to emphasize that, although our dislike of some award-winning visualizations is central to our argument, it is only the first part of our story. As Antony and I worked more on our paper, and especially after seeing the discussions by Robert Kosara, Stephen Few, Hadley Wickham, and Paul Murrell (all to appear in Journal of Computati
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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
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Introduction: Infovis and Statistical Graphics: Different Goals, Different Looks (and here’s the article) Speaker: Andrew Gelman, Columbia University Date: Thursday, November 29 2012 Time: 4:00PM to 5:00PM Location: 32-D463 (Star Conference Room) Host: Polina Golland, CSAIL Contact: Polina Golland, 6172538005, polina@csail.mit.edu The importance of graphical displays in statistical practice has been recognized sporadically in the statistical literature over the past century, with wider awareness following Tukey’s Exploratory Data Analysis (1977) and Tufte’s books in the succeeding decades. But statistical graphics still occupies an awkward in-between position: Within statistics, exploratory and graphical methods represent a minor subfield and are not well-integrated with larger themes of modeling and inference. Outside of statistics, infographics (also called information visualization or Infovis) is huge, but their purveyors and enthusiasts appear largely to be uninterested in statisti
Introduction: Our discussion on data visualization continues. One one side are three statisticians–Antony Unwin, Kaiser Fung, and myself. We have been writing about the different goals served by information visualization and statistical graphics. On the other side are graphics experts (sorry for the imprecision, I don’t know exactly what these people do in their day jobs or how they are trained, and I don’t want to mislabel them) such as Robert Kosara and Jen Lowe , who seem a bit annoyed at how my colleagues and myself seem to follow the Tufte strategy of criticizing what we don’t understand. And on the third side are many (most?) academic statisticians, econometricians, etc., who don’t understand or respect graphs and seem to think of visualization as a toy that is unrelated to serious science or statistics. I’m not so interested in the third group right now–I tried to communicate with them in my big articles from 2003 and 2004 )–but I am concerned that our dialogue with the graphic
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Introduction: Here . Indeed, I’d much rather be a legend than a myth. I just want to clarify one thing. Walter Hickey writes: [Antony Unwin and Andrew Gelman] collaborated on this presentation where they take a hard look at what’s wrong with the recent trends of data visualization and infographics. The takeaway is that while there have been great leaps in visualization technology, some of the visualizations that have garnered the highest praises have actually been lacking in a number of key areas. Specifically, the pair does a takedown of the top visualizations of 2008 as decided by the popular statistics blog Flowing Data. This is a fair summary, but I want to emphasize that, although our dislike of some award-winning visualizations is central to our argument, it is only the first part of our story. As Antony and I worked more on our paper, and especially after seeing the discussions by Robert Kosara, Stephen Few, Hadley Wickham, and Paul Murrell (all to appear in Journal of Computati
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Introduction: The most perfect pairing of author and subject since Nicholson Baker and John Updike. Here’s Wolfram on the great researcher of fractals : In his way, Mandelbrot paid me some great compliments. When I was in my 20s, and he in his 60s, he would ask about my scientific work: “How can so many people take someone so young so seriously?” In 2002, my book “A New Kind of Science”—in which I argued that many phenomena across science are the complex results of relatively simple, program-like rules—appeared. Mandelbrot seemed to see it as a direct threat, once declaring that “Wolfram’s ‘science’ is not new except when it is clearly wrong; it deserves to be completely disregarded.” In private, though, several mutual friends told me, he fretted that in the long view of history it would overwhelm his work. In retrospect, I don’t think Mandelbrot had much to worry about on this account. The link from the above review came from Peter Woit, who also points to a review by Brian Hayes wit
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Introduction: Denis Cote sends the following , under the heading, “Some bad graphs for your enjoyment”: To start with, they don’t know how to spell “color.” Seriously, though, the graph is a mess. The circular display implies a circular or periodic structure that isn’t actually in the data, the cramped display requires the use of an otherwise-unnecessary color code that makes it difficult to find or make sense of the information, the alphabetical ordering (without even supplying state names, only abbreviations) makes it further difficult to find any patterns. It would be so much better, and even easier, to just display a set of small maps shading states on whether they have different laws. But that’s part of the problem—the clearer graph would also be easier to make! To get a distinctive graph, there needs to be some degree of difficulty. The designers continue with these monstrosities: Here they decide to display only 5 states at a time so that it’s really hard to see any big pi
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Introduction: Duncan Watts writes: The Human Social Dynamics Group in Yahoo Research is seeking highly qualified candidates for a post-doctoral research scientist position. The Human and Social Dynamics group is devoted to understanding the interplay between individual-level behavior (e.g. how people make decisions about what music they like, which dates to go on, or which groups to join) and the social environment in which individual behavior necessarily plays itself out. In particular, we are interested in: * Structure and evolution of social groups and networks * Decision making, social influence, diffusion, and collective decisions * Networking and collaborative problem solving. The intrinsically multi-disciplinary and cross-cutting nature of the subject demands an eclectic range of researchers, both in terms of domain-expertise (e.g. decision sciences, social psychology, sociology) and technical skills (e.g. statistical analysis, mathematical modeling, computer simulations, design o
Introduction: Here’s some psychology research that’s relevant to yesterday’s discussion on working-class voting. In a paper to appear in the journal Cognitive Science , Andrei Cimpian, Amanda Brandone, and Susan Gelman write: Generic statements (e.g., “Birds lay eggs”) express generalizations about categories. In this paper, we hypothesized that there is a paradoxical asymmetry at the core of generic meaning, such that these sentences have extremely strong implications but require little evidence to be judged true. Four experiments confirmed the hypothesized asymmetry: Participants interpreted novel generics such as “Lorches have purple feathers” as referring to nearly all lorches, but they judged the same novel generics to be true given a wide range of prevalence levels (e.g., even when only 10% or 30% of lorches had purple feathers). A second hypothesis, also confirmed by the results, was that novel generic sentences about dangerous or distinctive properties would be more acceptable than
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