andrew_gelman_stats andrew_gelman_stats-2010 andrew_gelman_stats-2010-304 knowledge-graph by maker-knowledge-mining
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Introduction: A 24-hour student data visualization competition. The funny thing is, the actual graphics on the webpage are pretty ugly. But maybe they’re going for the retro, clip-art cool look.
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Introduction: A 24-hour student data visualization competition. The funny thing is, the actual graphics on the webpage are pretty ugly. But maybe they’re going for the retro, clip-art cool look.
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Introduction: This looks cool.
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: 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
Introduction: See here . Cool–it looks like they’re doing interesting stuff, and it’s great to see this sort of support for applied research.
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Introduction: A 24-hour student data visualization competition. The funny thing is, the actual graphics on the webpage are pretty ugly. But maybe they’re going for the retro, clip-art cool look.
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Introduction: Eytan Adar writes: I was just going through the latest draft of your paper with Anthony Unwin . I heard part of it at the talk you gave (remotely) here at UMich. I’m curious about your discussion of the Baby Name Voyager . The tool in itself is simple, attractive, and useful. No argument from me there. It’s an awesome demonstration of how subtle interactions can be very helpful (click and it zooms, type and it filters… falls perfectly into the Shneiderman visualization mantra). It satisfies a very common use case: finding appropriate names for children. That said, I can’t help but feeling that what you are really excited about is the very static analysis on last letters (you spend most of your time on this). This analysis, incidentally, is not possible to infer from the interactive application (which doesn’t support this type of filtering and pivoting). In a sense, the two visualizations don’t have anything to do with each other (other than a shared context/dataset).
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Introduction: A great new blog-class by Shawn Allen at Data Visualization , assembling all the good stuff in one place.
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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 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: Alex Hoffman points me to this interview by Dylan Matthews of education researcher Thomas Kane, who at one point says, Once you corrected for measurement error, a teacher’s score on their chosen videos and on their unchosen videos were correlated at 1. They were perfectly correlated. Hoffman asks, “What do you think? Do you think that just maybe, perhaps, it’s possible we aught to consider, I’m just throwing out the possibility that it might be that the procedure for correcting measurement error might, you now, be a little too strong?” I don’t know exactly what’s happening here, but it might be something that I’ve seen on occasion when fitting multilevel models using a point estimate for the group-level variance. It goes like this: measurement-error models are multilevel models, they involve the estimation of a distribution of a latent variable. When fitting multilevel models, it is possible to estimate the group-level variance to be zero, even though the group-level varia
Introduction: John Cook writes : When I hear someone say “personalized medicine” I want to ask “as opposed to what?” All medicine is personalized. If you are in an emergency room with a broken leg and the person next to you is lapsing into a diabetic coma, the two of you will be treated differently. The aim of personalized medicine is to increase the degree of personalization, not to introduce personalization. . . . This to me is a statistical way of thinking, to change an “Is it or isn’t it?” question into a “How much?” question. This distinction arises in many settings but particularly in discussions of causal inference, for example here and here , where I use the “statistical thinking” approach of imagining everything as being on some continuous scale, in contrast to computer scientist Elias Bareinboim and psychology researcher Steven Sloman, both of whom prefer what might be called the “civilian” or “common sense” idea that effects are either real or not, or that certain data can
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