andrew_gelman_stats andrew_gelman_stats-2012 andrew_gelman_stats-2012-1450 knowledge-graph by maker-knowledge-mining
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Introduction: Somebody asked me to speak sometime at a data visualization meetup. I think I spoke there a year or two ago but I could do it again. Last time I spoke on Infovis vs Statistical Graphics , this time I could just go thru the choices involved in a few zillion graphs I’ve published over the years, to give a sense of the options and choices involved in graphical communication. For this talk there would be no single theme (except, perhaps, my usual “Graphs as comparisons,” “All of statistics as comparisons,” and “Exploratory data analysis as hypothesis testing”), just a bunch of open discussion about what I tried, why I tried it, what worked and what didn’t work, etc. I’ve discussed these sorts of decisions on occasion (and am now writing a paper with Yair about some of this for our voting models), but I’ve never tried to make a talk out of it before. Could be fun.
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Introduction: Somebody asked me to speak sometime at a data visualization meetup. I think I spoke there a year or two ago but I could do it again. Last time I spoke on Infovis vs Statistical Graphics , this time I could just go thru the choices involved in a few zillion graphs I’ve published over the years, to give a sense of the options and choices involved in graphical communication. For this talk there would be no single theme (except, perhaps, my usual “Graphs as comparisons,” “All of statistics as comparisons,” and “Exploratory data analysis as hypothesis testing”), just a bunch of open discussion about what I tried, why I tried it, what worked and what didn’t work, etc. I’ve discussed these sorts of decisions on occasion (and am now writing a paper with Yair about some of this for our voting models), but I’ve never tried to make a talk out of it before. Could be fun.
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Introduction: It’s in midtown at 7pm (on Mon 14 Jan 2013). Last time I talked for this group, I spoke on Infovis vs. Statistical Graphics . This time I plan to just go thru the choices involved in a few zillion graphs I’ve published over the years, to give a sense of the options and choices involved in graphical communication. For this talk there will be no single theme (except, perhaps, my usual “Graphs as comparisons,” “All of statistics as comparisons,” and “Exploratory data analysis as hypothesis testing”), just a bunch of open discussion about what I tried, why I tried it, what worked and what didn’t work, etc. I’ve discussed these sorts of decisions on occasion (and am now writing a paper with Yair about some of this for our voting models), but I’ve never tried to make a talk out of it before. Could be fun.
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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
5 0.18540956 2266 andrew gelman stats-2014-03-25-A statistical graphics course and statistical graphics advice
Introduction: Dean Eckles writes: Some of my coworkers at Facebook and I have worked with Udacity to create an online course on exploratory data analysis, including using data visualizations in R as part of EDA. The course has now launched at https://www.udacity.com/course/ud651 so anyone can take it for free. And Kaiser Fung has reviewed it . So definitely feel free to promote it! Criticism is also welcome (we are still fine-tuning things and adding more notes throughout). I wrote some more comments about the course here , including highlighting the interviews with my great coworkers. I didn’t have a chance to look at the course so instead I responded with some generic comments about eda and visualization (in no particular order): - Think of a graph as a comparison. All graphs are comparison (indeed, all statistical analyses are comparisons). If you already have the graph in mind, think of what comparisons it’s enabling. Or if you haven’t settled on the graph yet, think of what
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Introduction: It’s in midtown at 7pm (on Mon 14 Jan 2013). Last time I talked for this group, I spoke on Infovis vs. Statistical Graphics . This time I plan to just go thru the choices involved in a few zillion graphs I’ve published over the years, to give a sense of the options and choices involved in graphical communication. For this talk there will be no single theme (except, perhaps, my usual “Graphs as comparisons,” “All of statistics as comparisons,” and “Exploratory data analysis as hypothesis testing”), just a bunch of open discussion about what I tried, why I tried it, what worked and what didn’t work, etc. I’ve discussed these sorts of decisions on occasion (and am now writing a paper with Yair about some of this for our voting models), but I’ve never tried to make a talk out of it before. Could be fun.
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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: I have this great talk on the above topic but nowhere to give it. Here’s the story. Several months ago, I was invited to speak at IEEE VisWeek. It sounded like a great opportunity. The organizer told me that there were typically about 700 people in the audience, and these are people in the visualization community whom I’d like to reach but normally wouldn’t have the opportunity to encounter. It sounded great, but I didn’t want to fly most of the way across the country by myself, so I offered to give the talk by videolink. I was surprised to get a No response: I’d think that a visualization conference, of all things, would welcome a video talk. In the meantime, though, I’d thought a lot about what I’d talk about and had started preparing something. Once I found out I wouldn’t be giving the talk, I channeled the efforts into an article which, with the collaboration of Antony Unwin, was completed about a month ago. It would take very little effort to adapt this graph-laden a
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Introduction: I just gave a talk in Milan. Actually I was sitting at my desk, it was a g+ hangout which was a bit more convenient for me. The audience was a bunch of astronomers so I figured they could handle a satellite link. . . . Anyway, the talk didn’t go so well. Two reasons: first, it’s just hard to get the connection with the audience without being able to see their faces. Next time I think I’ll try to get several people in the audience to open up their laptops and connect to the hangout, so that I can see a mosaic of faces instead of just a single image from the front of the room. The second problem with the talk was the topic. I asked the people who invited me to choose a topic, and they picked Can we use Bayesian methods to resolve the current crisis of statistically-significant research findings that don’t hold up? But I don’t think this was right for this audience. I think that it would’ve been better to give them the Stan talk or the little data talk or the statistic
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Introduction: It’s in midtown at 7pm (on Mon 14 Jan 2013). Last time I talked for this group, I spoke on Infovis vs. Statistical Graphics . This time I plan to just go thru the choices involved in a few zillion graphs I’ve published over the years, to give a sense of the options and choices involved in graphical communication. For this talk there will be no single theme (except, perhaps, my usual “Graphs as comparisons,” “All of statistics as comparisons,” and “Exploratory data analysis as hypothesis testing”), just a bunch of open discussion about what I tried, why I tried it, what worked and what didn’t work, etc. I’ve discussed these sorts of decisions on occasion (and am now writing a paper with Yair about some of this for our voting models), but I’ve never tried to make a talk out of it before. Could be fun.
Introduction: Remember How to Lie With Statistics? It turns out that the author worked for the cigarette companies. John Mashey points to this, from Robert Proctor’s book, “Golden Holocaust: Origins of the Cigarette Catastrophe and the Case for Abolition”: Darrell Huff, author of the wildly popular (and aptly named) How to Lie With Statistics, was paid to testify before Congress in the 1950s and then again in the 1960s, with the assigned task of ridiculing any notion of a cigarette-disease link. On March 22, 1965, Huff testified at hearings on cigarette labeling and advertising, accusing the recent Surgeon General’s report of myriad failures and “fallacies.” Huff peppered his attack with with amusing asides and anecdotes, lampooning spurious correlations like that between the size of Dutch families and the number of storks nesting on rooftops–which proves not that storks bring babies but rather that people with large families tend to have larger houses (which therefore attract more storks).
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Introduction: Several years ago, I heard about a project at the Educational Testing Service to identify “strivers”: students from disadvantaged backgrounds who did unexpectedly well on the SAT (the college admissions exam formerly known as the “Scholastic Aptitude Test” but apparently now just “the SAT,” in the same way that Exxon is just “Exxon” and that Harry Truman’s middle name is just “S”), at least 200 points above a predicted score based on demographic and neighborhood information. My ETS colleague and I agreed that this was a silly idea: From a statistical point of view, if student A is expected ahead of time to do better than student B, and then they get identical test scores, then you’d expect student A (the non-”striver”) to do better than student B (the “striver”) later on. Just basic statistics: if a student does much better than expected, then probably some of that improvement is noise. The idea of identifying these “strivers” seemed misguided and not the best use of the SAT.
4 0.95980883 1261 andrew gelman stats-2012-04-12-The Naval Research Lab
Introduction: I worked at the U.S. Naval Research Laboratory for four summers during high school and college. I spent much of my time writing a computer program to do thermal analysis for an experiment that we put on the space shuttle. The facility I developed with the finite-element method came in handy in my job at Bell Labs the following summers. I was working for C. H. Tsao and Jim Adams in the Laboratory for Cosmic Ray Physics. We were estimating the distribution of isotopes in cosmic rays using a pile of track detectors. To get accurate measurements, you want these plastic disks to be as close as possible to a constant temperature, so we designed an elaborate wrapping of thermal blankets. My program computed the temperature of the detectors during the year that the Long Duration Exposure Facility (including our experiment and a bunch of others) was scheduled to be in orbit. The input is the heat from solar radiation (easy enough to compute given the trajectory). On the computer I tr
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Introduction: Phil Plait writes : Earth May Have Been Hit by a Cosmic Blast 1200 Years Ago . . . this is nothing to panic about. If it happened at all, it was a long time ago, and unlikely to happen again for hundreds of thousands of years. This left me confused. If it really did happen 1200 years ago, basic statistics would suggest it would occur approximately once every 1200 years or so (within half an order of magnitude). So where does “hundreds of thousands of years” come from? I emailed astronomer David Hogg to see if I was missing something here, and he replied: Yeah, if we think this hit us 1200 years ago, we should imagine that this happens every few thousand years at least. Now that said, if there are *other* reasons for thinking it is exceedingly rare, then that would be a strong a priori argument against believing in the result. So you should either believe that it didn’t happen 1200 years ago, or else you should believe it will happen again in the next few thousan
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