andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-1669 knowledge-graph by maker-knowledge-mining
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Introduction: The Organisation for Economic Co-operation and Development reports that the following project from Krisztina Szucs and Mate Cziner has won their visualization challenge, “launched in September 2012 to solicit visualisations based on the OECD’s data-rich Education at a Glance report”: (The graph is interactive. Click on the above image and click again to see the full version.) From the press release: Entries from around the world focused on data related to the economic costs and return on investment in education . . . [The winning entry] takes a detailed look at public vs. private and men vs. women for selected countries . . . The judges were particularly impressed by the angled slope format of the visualisation, which encourages comparison between the upper-secondary and tertiary benefits of education. Szucs and Cziner were also lauded for their striking visual design, which draws users into exploring their piece [emphasis added]. I used boldface to highlight a p
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1 Click on the above image and click again to see the full version. [sent-2, score-0.113]
2 ) From the press release: Entries from around the world focused on data related to the economic costs and return on investment in education . [sent-3, score-0.45]
3 [The winning entry] takes a detailed look at public vs. [sent-6, score-0.296]
4 The judges were particularly impressed by the angled slope format of the visualisation, which encourages comparison between the upper-secondary and tertiary benefits of education. [sent-11, score-0.312]
5 Szucs and Cziner were also lauded for their striking visual design, which draws users into exploring their piece [emphasis added]. [sent-12, score-0.106]
6 To be frank, I don’t think many people will actually learn much at all about education or education statistics from playing with the Szucs and Cziner visualization, but it does a good job of selling the topic in the sense of making the numbers look potentially interesting and surprising. [sent-15, score-0.667]
7 More here from Patrick Love at the OECD, who writes: They chose Krisztina and Maté’s graph from over 30 entries because it “does a great job of breaking down the complex interplay between costs and returns into a form that is easy to compare”. [sent-16, score-0.794]
8 And also because “instead of the many-country approach used by most entries, the project takes a detailed look at public vs. [sent-17, score-0.387]
9 women for three selected countries (which you can change)”. [sent-19, score-0.442]
10 That I can’t see the benefit of, given that the graph is interactive. [sent-23, score-0.11]
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Introduction: The Organisation for Economic Co-operation and Development reports that the following project from Krisztina Szucs and Mate Cziner has won their visualization challenge, “launched in September 2012 to solicit visualisations based on the OECD’s data-rich Education at a Glance report”: (The graph is interactive. Click on the above image and click again to see the full version.) From the press release: Entries from around the world focused on data related to the economic costs and return on investment in education . . . [The winning entry] takes a detailed look at public vs. private and men vs. women for selected countries . . . The judges were particularly impressed by the angled slope format of the visualisation, which encourages comparison between the upper-secondary and tertiary benefits of education. Szucs and Cziner were also lauded for their striking visual design, which draws users into exploring their piece [emphasis added]. I used boldface to highlight a p
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Introduction: I followed a link from Tyler Cowen to this bit by Daniel Kahneman: Education is an important determinant of income — one of the most important — but it is less important than most people think. If everyone had the same education, the inequality of income would be reduced by less than 10%. When you focus on education you neglect the myriad other factors that determine income. The differences of income among people who have the same education are huge. I think I know what he’s saying–if you regress income on education and other factors, and then you take education out of the model, R-squared decreases by 10%. Or something like that. Not necessarily R-squared, maybe you fit the big model, then get predictions for everyone putting in the mean value for education and look at the sd of incomes or the Gini index or whatever. Or something else along those lines. My problem is with the counterfactual: “If everyone had the same education . . .” I have a couple problems with this
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Introduction: I recently came across a data visualization that perfectly demonstrates the difference between the “infovis” and “statgraphics” perspectives. Here’s the image ( link from Tyler Cowen): That’s the infovis. The statgraphic version would simply be a dotplot, something like this: (I purposely used the default settings in R with only minor modifications here to demonstrate what happens if you just want to plot the data with minimal effort.) Let’s compare the two graphs: From a statistical graphics perspective, the second graph dominates. The countries are directly comparable and the numbers are indicated by positions rather than area. The first graph is full of distracting color and gives the misleading visual impression that the total GDP of countries 5-10 is about equal to that of countries 1-4. If the goal is to get attention , though, it’s another story. There’s nothing special about the top graph above except how it looks. It represents neither a dat
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 0.10026252 583 andrew gelman stats-2011-02-21-An interesting assignment for statistical graphics
Introduction: Antony Unwin writes: I [Unwin] find it an interesting exercise for students to ask them to write headlines (and subheadlines) for graphics, both for ones they have drawn themselves and for published ones. The results are sometimes depressing, often thought-provoking and occasionally highly entertaining. This seems like a great idea, both for teaching students how to read a graph and also for teaching how to make a graph. I’ve long said that when making a graph (or, for that matter, a table), you want to think about what message the reader will get out of it. “Displaying a bunch of numbers” doesn’t cut it.
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Introduction: The Organisation for Economic Co-operation and Development reports that the following project from Krisztina Szucs and Mate Cziner has won their visualization challenge, “launched in September 2012 to solicit visualisations based on the OECD’s data-rich Education at a Glance report”: (The graph is interactive. Click on the above image and click again to see the full version.) From the press release: Entries from around the world focused on data related to the economic costs and return on investment in education . . . [The winning entry] takes a detailed look at public vs. private and men vs. women for selected countries . . . The judges were particularly impressed by the angled slope format of the visualisation, which encourages comparison between the upper-secondary and tertiary benefits of education. Szucs and Cziner were also lauded for their striking visual design, which draws users into exploring their piece [emphasis added]. I used boldface to highlight a p
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Introduction: Under the subject line “Blog bait!”, Brendan Nyhan points me to this post at the Washington Post blog: For 2013, we asked some of the year’s most interesting, important and influential thinkers to name their favorite graph of the year — and why they chose it. Here’s Bill Gates’s. Infographic by Thomas Porostocky for WIRED. “I love this graph because it shows that while the number of people dying from communicable diseases is still far too high, those numbers continue to come down. . . .” As Brendan is aware, this is not my favorite sort of graph, it’s a bit of a puzzle to read and figure out where all the pieces fit in, also weird stuff going on like 3-D effects and the big space taken up by those yellow and green borders, as well as tricky things like understanding what some of those little blocks are, and perhaps the biggest question, what is the definition of an “untimely death.” But, as often is the case, the defects of the graph form a statistical perspective can
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Introduction: Ricardo Pietrobon writes, regarding my post from last year on attitudes toward data graphics, Wouldn’t it be the case to start formally studying the usability of graphics from a cognitive perspective? with platforms such as the mechanical turk it should be fairly straightforward to test alternative methods and come to some conclusions about what might be more informative and what might better assist in supporting decisions. btw, my guess is that these two constructs might not necessarily agree with each other. And Jessica Hullman provides some background: Measuring success for the different goals that you hint at in your article is indeed challenging, and I don’t think that most visualization researchers would claim to have met this challenge (myself included). Visualization researchers may know the user psychology well when it comes to certain dimensions of a graph’s effectiveness (such as quick and accurate responses), but I wouldn’t agree with this statement as a gene
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Introduction: Paul Alper writes: You recently posted my moving and widening the goalposts contention. In it, I mentioned “how diagnoses increase markedly while deaths are flatlined” indicating that we are being overdiagnosed and overtreated. Above are 5 frightening graphs which illustrate the phenomenon. Defenders of the system might (ludicrously) contend that it is precisely the aggressive medical care that is responsible for keeping the cancers under control. The prostate cancer graph is particularly interesting because it shows the peaking of the PSA-driven cause of treatment in the 1990s which then falls off as the evidence accumulates that the PSA was far from a perfect indicator. In contrast is the thyroid cancer which zooms skyward even as the death rate is absolutely (dead) flat. And of course here’s the famous cross-country comparison that some find “ schlocky ” but which I (and many others) find compelling :
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Introduction: John Cook and Joseph Delaney point to an article by Yurii Aulchenko et al., who write: 54 loci showing strong statistical evidence for association to human height were described, providing us with potential genomic means of human height prediction. In a population-based study of 5748 people, we find that a 54-loci genomic profile explained 4-6% of the sex- and age-adjusted height variance, and had limited ability to discriminate tall/short people. . . . In a family-based study of 550 people, with both parents having height measurements, we find that the Galtonian mid-parental prediction method explained 40% of the sex- and age-adjusted height variance, and showed high discriminative accuracy. . . . The message is that the simple approach of predicting child’s height using a regression model given parents’ average height performs much better than the method they have based on combining 54 genes. They also find that, if you start with the prediction based on parents’ heigh
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Introduction: With all this data floating around, there are some interesting analyses one can do. I came across “The Association of Tree Pollen Concentration Peaks and Allergy Medication Sales in New York City: 2003-2008″ by Perry Sheffield . There they correlate pollen counts with anti-allergy medicine sales – and indeed find that two days after high pollen counts, the medicine sales are the highest. Of course, it would be interesting to play with the data to see *what* tree is actually causing the sales to increase the most. Perhaps this would help the arborists what trees to plant. At the moment they seem to be following a rather sexist approach to tree planting: Ogren says the city could solve the problem by planting only female trees, which don’t produce pollen like male trees do. City arborists shy away from females because many produce messy – or in the case of ginkgos, smelly – fruit that litters sidewalks. In Ogren’s opinion, that’s a mistake. He says the females only pro
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Introduction: The Organisation for Economic Co-operation and Development reports that the following project from Krisztina Szucs and Mate Cziner has won their visualization challenge, “launched in September 2012 to solicit visualisations based on the OECD’s data-rich Education at a Glance report”: (The graph is interactive. Click on the above image and click again to see the full version.) From the press release: Entries from around the world focused on data related to the economic costs and return on investment in education . . . [The winning entry] takes a detailed look at public vs. private and men vs. women for selected countries . . . The judges were particularly impressed by the angled slope format of the visualisation, which encourages comparison between the upper-secondary and tertiary benefits of education. Szucs and Cziner were also lauded for their striking visual design, which draws users into exploring their piece [emphasis added]. I used boldface to highlight a p
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Introduction: In politics we’re familiar with the non-apology apology (well described in Wikipedia as “a statement that has the form of an apology but does not express the expected contrition”). Here’s the scientific equivalent: the non-retraction retraction. Sanjay Srivastava points to an amusing yet barfable story of a pair of researchers who (inadvertently, I assume) made a data coding error and were eventually moved to issue a correction notice, but even then refused to fully admit their error. As Srivastava puts it, the story “ended up with Lew [Goldberg] and colleagues [Kibeom Lee and Michael Ashton] publishing a comment on an erratum – the only time I’ve ever heard of that happening in a scientific journal.” From the comment on the erratum: In their “erratum and addendum,” Anderson and Ones (this issue) explained that we had brought their attention to the “potential” of a “possible” misalignment and described the results computed from re-aligned data as being based on a “post-ho
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