andrew_gelman_stats andrew_gelman_stats-2012 andrew_gelman_stats-2012-1096 knowledge-graph by maker-knowledge-mining
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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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1 Lee Epstein has written a pair of wonderful articles on graphic reporting – 1. [sent-2, score-0.719]
2 – but her efforts haven’t gotten the attention they deserve, and reinforcement, particularly at a venue like CELS is very important. [sent-20, score-0.208]
3 The more difficult points involve selection and integration of graphic reporting methods given the array of audiences legal scholars are likely to be targeting. [sent-36, score-1.407]
4 These include (A) other scholars doing empirical legal studies, (B) other scholars who don’t use empirical methods and (C) lawmakers, judges, and lawyers. [sent-37, score-1.082]
5 You sensibly said to me that the style of graphic reporting one uses depends on one’s purposes. [sent-38, score-0.784]
6 For purposes of communicating with *any* of these constinuencies, the info-whatever USA Today stuff is not appropriate. [sent-39, score-0.286]
7 But I don’t think there’s any *one* graphic reporting method that will always suit the purpose of communicating with all three of these groups of readers, and we often *are* writing for all three at once. [sent-40, score-1.067]
8 The nub of the problem, as I see it, is that the sort of graphic reporting you are extremely good at is aimed at communicating with (A). [sent-41, score-1.074]
9 But the graphic reporting that is ideal for them is often *not* the best for readers in the (B) & (C) classes. [sent-43, score-0.869]
10 This is in part b/c the best graphics for (A) communicate certain concepts that (B) & (C) likely don’t understand. [sent-44, score-0.366]
11 But it is also true b/c those readers, particularly ones in (C), are likely to be modest in numeracy, and likely to fail to understand or comprehend the significance of information that is reported in the graphics that are ideal for (A). [sent-45, score-0.545]
12 I know you get this issue– you negotiated it very well, e. [sent-47, score-0.069]
13 I think it would have been great for CELS attendees to have the chance to explore these issues w/ you in a concrete way, one focused on examples you have deal with & that we have faced. [sent-50, score-0.058]
14 Also, I’d love to know what you make of the sort of research on communicating statistical information that Spiegelhalter, D. [sent-51, score-0.426]
15 I don’t have any great reply here; really, any good answer would require experience in communication that I don’t have. [sent-56, score-0.142]
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same-blog 1 0.99999982 1096 andrew gelman stats-2012-01-02-Graphical communication for legal scholarship
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: When I first saw this graphic, I thought “boy, that’s great, sometimes the graphic practically makes itself.” Normally it’s hard to use lots of different colors to differentiate items of interest, because there’s usually not an intuitive mapping between color and item (e.g. for countries, or states, or whatever). But the colors of crayons, what could be more perfect? So this graphic seemed awesome. But, as they discovered after some experimentation at datapointed.net there is an even BETTER possibility here. Click the link to see. Crayola Crayon colors by year
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Introduction: Ironically, I can’t find the source for this awesome graphic that’s been making the rounds. -Phil
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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: 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: 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: 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: 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: One of the most satisfying experiences for an academic is when someone asks a question that you’ve already answered. This happened in the comments today. Daniel Gotthardt wrote : So for applied stat courses like for sociologists, political scientists, psychologists and maybe also for economics, what do we actually want to accomplish with our intro courses? And how would it help to include Bayesian Statistics in them? And I was like, hey! This reminds me of a paper I published a few years ago, “Teaching Bayesian applied statistics to graduate students in political science, sociology, public health, education, economics, . . .” Here it is , and it begins as follows: I was trying to draw Bert and Ernie the other day, and it was really difficult. I had pictures of them right next to me, but my drawings were just incredibly crude, more “linguistic” than “visual” in the sense that I was portraying key aspects of Bert and Ernie but in pictures that didn’t look anything like t
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Introduction: Seth, a retired university professor who, during his employment at an elite school, spent a lot of time doing research with the goal of improving people’s lives, writes : Professors, especially at elite schools, dislike doing research with obvious value. It strikes them as menial. “Practical” and “applied” are terms of disparagement, whereas “pure” research (research without obvious value) is good. Given that Seth isn’t that way himself, I assume he’d say that this claim applies to “many” professors or “most” professors but surely not all? What I’ve noticed, though, is more the opposite, that even people who do extremely theoretical work like to feel that it is applied, practical, and useful. I think that, among other things, Seth is confusing what people want to do with what they actually can do . For example, he criticizes biologists for researching stem cells and prions rather than prevention of disease. But preventing diseases is difficult! That’s why the scientists
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Introduction: Rachel Schutt (the author of the Taxonomy of Confusion) has a blog! for the course she’s teaching at Columbia, “Introduction to Data Science.” It sounds like a great course—I wish I could take it! Her latest post is “On Inspiring Students and Being Human”: Of course one hopes as a teacher that one will inspire students . . . But what I actually mean by “inspiring students” is that you are inspiring me; you are students who inspire: “inspiring students”. This is one of the happy unintended consequences of this course so far for me. She then gives examples of some of the students in her class and some of their interesting ideas: Phillip is a PhD student in the sociology department . . . He’s in the process of developing his thesis topic around some of the themes we’ve been discussing in this class, such as the emerging data science community. Arvi works at the College Board and is a part time student . . . He analyzes user-level data of students who have signed up f
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