andrew_gelman_stats andrew_gelman_stats-2012 andrew_gelman_stats-2012-1594 knowledge-graph by maker-knowledge-mining
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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
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1 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. [sent-3, score-1.175]
2 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. [sent-4, score-1.116]
3 Outside of statistics, infographics (also called information visualization or Infovis) is huge, but their purveyors and enthusiasts appear largely to be uninterested in statistical principles. [sent-5, score-0.58]
4 We present a set of goals for graphical displays discussed primarily from the statistical point of view and discuss some inherent contradictions in these goals that may be impeding communication between the fields of statistics and Infovis. [sent-6, score-1.471]
5 One of our constructive suggestions, to Infovis practitioners and statisticians alike, is to try not to cram into a single graph what can be better displayed in two or more. [sent-7, score-0.463]
6 We recognize that we offer only one perspective and intend this work to be a starting point for a wide-ranging discussion among graphics designers, statisticians, and users of statistical methods. [sent-8, score-0.487]
7 Our purpose is not to criticize but to explore the different goals that lead researchers in different fields to value different aspects of data visualization. [sent-9, score-0.807]
8 Following my recent thoughts, I wish I’d called it Tradeoffs in information graphics . [sent-12, score-0.315]
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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
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Introduction: Stan: open-source Bayesian inference Speaker: Andrew Gelman, Columbia University Date: Thursday, October 11 2012 Time: 4:00PM to 5:00PM Location: 32-D507 Host: Polina Golland, CSAIL Contact: Polina Golland, 6172538005, polina@csail.mit.edu Stan ( mc-stan.org ) is an open-source package for obtaining Bayesian inference using the No-U-Turn sampler, a variant of Hamiltonian Monte Carlo. We discuss how Stan works and what it can do, the problems that motivated us to write Stan, current challenges, and areas of planned development, including tools for improved generality and usability, more efficient sampling algorithms, and fuller integration of model building, model checking, and model understanding in Bayesian data analysis. P.S. Here’s the talk .
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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: 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
Introduction: I continue to struggle to convey my thoughts on statistical graphics so I’ll try another approach, this time giving my own story. For newcomers to this discussion: the background is that Antony Unwin and I wrote an article on the different goals embodied in information visualization and statistical graphics, but I have difficulty communicating on this point with the infovis people. Maybe if I tell my own story, and then they tell their stories, this will point a way forward to a more constructive discussion. So here goes. I majored in physics in college and I worked in a couple of research labs during the summer. Physicists graph everything. I did most of my plotting on graph paper–this continued through my second year of grad school–and became expert at putting points at 1/5, 2/5, 3/5, and 4/5 between the x and y grid lines. In grad school in statistics, I continued my physics habits and graphed everything I could. I did notice, though, that the faculty and the other
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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
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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: 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: Infovis vs. statistical graphics . Tues 1 Feb 2011 1pm, Avery Hall room 114. It’s for the Lectures in Planning Series at the School of Architecture, Planning, and Preservation. Background on the talk (joint with Antony Unwin) is here . And here are more of my thoughts on statistical graphics.
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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: Image via Wikipedia Robert Zubrin writes in “How Much Is an Astronaut’s Life Worth?” ( Reason , Feb 2012 ): …policy analyst John D. Graham and his colleagues at the Harvard Center for Risk Analysis found in 1997 that the median cost for lifesaving expenditures and regulations by the U.S. government in the health care, residential, transportation, and occupational areas ranges from about $1 million to $3 million spent per life saved in today’s dollars. The only marked exception to this pattern occurs in the area of environmental health protection (such as the Superfund program) which costs about $200 million per life saved. Graham and his colleagues call the latter kind of inefficiency “ statistical murder ,” since thousands of additional lives could be saved each year if the money were used more cost-effectively. To avoid such deadly waste, the Department of Transportation has a policy of rejecting any proposed safety expenditure that costs more than $3
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Introduction: Johnny Carson had this great trick where, after a joke bombed, he’d do such a good double-take that he’d end up getting a huge laugh. This gimmick could never have worked as his sole shtick—at some point, Johnny had to tell some good jokes—but it was a reliable way to limit the downside. For the purpose of our discussion here, the point is that, even when the joke failed, Carson had a way out. I thought of this today after following a link from a commenter that led to this blog on publicity-minded author Tim Ferriss. I’ve never read anything by Ferriss but I’ve read about him on occasion: his gimmick is he promotes his book using ingenious marketing strategies. Sort of like how Madonna is famous for being famous, and Paris Hilton is famous for being famous for being famous, Ferriss is famous for self-promotion. Matt Metzgar writes : I [Metzgar] saw a bunch of ads on the internet today for Tim Ferriss’ new book. Even though the book was released today, it already has all
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Introduction: Robert Bell writes: I [Bell] just finished “The Emperor of All Maladies” from 2010. One of the sections is captioned with the “In God we trust, all others bring data” quote which is supposedly from Deming. If you haven’t read it, I think you might like it even though the topic is pretty morbid (i.e. cancer). I was rereading chapter 7 in Bayesian Data Analysis. While the principles there would allow me to “roll my own”, I was wondering whether or not there is now a comprehensive “cookbook” out there that gathers together such analyses for a variety of experimental designs – sort of a Bayesian equivalent to Montgomery’s “Design and Analysis of Experiments”. My reply: I don’t know, but now I’m thinking we should put some of this in our forthcoming book, Statistics for Psychology Research. In the meantime, if anyone has any suggestions, feel free to post them in comments.
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