andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-514 knowledge-graph by maker-knowledge-mining
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Introduction: This post is by Phil Price. A reporter once told me that the worst-kept secret of journalism is that every story has errors. And it’s true that just about every time I know about something first-hand, the news stories about it have some mistakes. Reporters aren’t subject-matter experts, they have limited time, and they generally can’t keep revisiting the things they are saying and checking them for accuracy. Many of us have published papers with errors — my most recent paper has an incorrect figure — and that’s after working on them carefully for weeks! One way that reporters can try to get things right is by quoting experts. Even then, there are problems with taking quotes out of context, or with making poor choices about what material to include or exclude, or, of course, with making a poor selection of experts. Yesterday, I was interviewed by an NPR reporter about the risks of breathing radon (a naturally occurring radioactive gas): who should test for it, how dangerous
sentIndex sentText sentNum sentScore
1 A reporter once told me that the worst-kept secret of journalism is that every story has errors. [sent-2, score-0.475]
2 And it’s true that just about every time I know about something first-hand, the news stories about it have some mistakes. [sent-3, score-0.081]
3 Reporters aren’t subject-matter experts, they have limited time, and they generally can’t keep revisiting the things they are saying and checking them for accuracy. [sent-4, score-0.193]
4 Many of us have published papers with errors — my most recent paper has an incorrect figure — and that’s after working on them carefully for weeks! [sent-5, score-0.284]
5 One way that reporters can try to get things right is by quoting experts. [sent-6, score-0.358]
6 Even then, there are problems with taking quotes out of context, or with making poor choices about what material to include or exclude, or, of course, with making a poor selection of experts. [sent-7, score-0.664]
7 Yesterday, I was interviewed by an NPR reporter about the risks of breathing radon (a naturally occurring radioactive gas): who should test for it, how dangerous is it, etc. [sent-8, score-1.355]
8 I’m a reasonable person to talk to about this, having done my post-doc and several subsequent years of research in this area, although that ended about ten years ago. [sent-9, score-0.317]
9 Andrew and I, and other colleagues, published several papers, including a decision analysis paper that encompasses most of what I think I know about radon risk in the U. [sent-10, score-0.841]
10 He had a much more sophisticated understanding than most reporters, and perhaps more than some radon researchers! [sent-13, score-0.639]
11 But I gave him a lot of “on the one hand…, on the other hand…” material, so if he quotes selectively he could make me look extreme in either direction. [sent-15, score-0.254]
12 The piece will be on NPR’s Morning Edition tomorrow (Friday), and available on their archives afterwards. [sent-17, score-0.338]
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Introduction: This post is by Phil Price. A reporter once told me that the worst-kept secret of journalism is that every story has errors. And it’s true that just about every time I know about something first-hand, the news stories about it have some mistakes. Reporters aren’t subject-matter experts, they have limited time, and they generally can’t keep revisiting the things they are saying and checking them for accuracy. Many of us have published papers with errors — my most recent paper has an incorrect figure — and that’s after working on them carefully for weeks! One way that reporters can try to get things right is by quoting experts. Even then, there are problems with taking quotes out of context, or with making poor choices about what material to include or exclude, or, of course, with making a poor selection of experts. Yesterday, I was interviewed by an NPR reporter about the risks of breathing radon (a naturally occurring radioactive gas): who should test for it, how dangerous
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Introduction: This post is by Phil. I’m aware that there are some people who use a Bayesian approach largely because it allows them to provide a highly informative prior distribution based subjective judgment, but that is not the appeal of Bayesian methods for a lot of us practitioners. It’s disappointing and surprising, twenty years after my initial experiences, to still hear highly informed professional statisticians who think that what distinguishes Bayesian statistics from Frequentist statistics is “subjectivity” ( as seen in a recent blog post and its comments ). My first encounter with Bayesian statistics was just over 20 years ago. I was a postdoc at Lawrence Berkeley National Laboratory, with a new PhD in theoretical atomic physics but working on various problems related to the geographical and statistical distribution of indoor radon (a naturally occurring radioactive gas that can be dangerous if present at high concentrations). One of the issues I ran into right at the start was th
Introduction: I came across a document [updated link here ], “Applying structured analogies to the global warming alarm movement,” by Kesten Green and Scott Armstrong. The general approach is appealing to me, but the execution seemed disturbingly flawed. Here’s how they introduce the project: The structured analogies procedure we [Green and Armstrong] used for this study was as follows: 1. Identify possible analogies by searching the literature and by asking experts with different viewpoints to nominate analogies to the target situation: alarm over dangerous manmade global warming. 2. Screen the possible analogies to ensure they meet the stated criteria and that the outcomes are known. 3. Code the relevant characteristics of the analogous situations. 4. Forecast target situation outcomes by using a predetermined mechanical rule to select the outcomes of the analogies. Here is how we posed the question to the experts: The Intergovernmental Panel on Climate Change and other organizat
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Introduction: Kaiser writes thoughtfully about the costs, benefits, and incentives for different policy recommendation options regarding a recent water crisis. Good stuff: it’s solid “freakonomics”–and I mean this in positive way: a mix of economic and statistical analysis, with assumptions stated clearly. Kaiser writes: Using the framework from Chapter 4, we should think about the incentives facing the Mass. Water Resources Authority: A false positive error (people asked to throw out water when water is clean) means people stop drinking tap water temporarily, perhaps switching to bottled water, and the officials claim victory when no one falls sick, and businesses that produce bottled water experience a jump in sales. It is also very difficult to prove a “false positive” when people have stopped drinking the water. So this type of error is easy to hide behind. A false negative error (people told it’s safe to drink water when water is polluted) becomes apparent when someone falls sick
Introduction: Sanjay Kaul writes: I am sure you must be aware of the recent controversy ignited by the 2013 American College of Cardiology/American Heart Association Cholesterol Treatment Guidelines that were released last month. They have been the subject of several newspaper articles and blogs , most of them missing the thrust of the guidelines. There is much to admire about these guidelines as they are more faithfully aligned with high-quality ‘actionable’ evidence than the 3 previous iterations. However, the controversy is focused on the performance of the risk calculator introduced for initiating treatment in individuals without established atherosclerotic disease or diabetes (so-called primary prevention cohort). The guidelines recommend statins for primary prevention in individuals who have a 10-year risk estimated to be 7.5%. The risk calculator was derived from population cohorts studied in the 1990s. The discrimination for predicting the risk of atherosclerotic cardiovascular events
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Introduction: This post is by Phil Price. A reporter once told me that the worst-kept secret of journalism is that every story has errors. And it’s true that just about every time I know about something first-hand, the news stories about it have some mistakes. Reporters aren’t subject-matter experts, they have limited time, and they generally can’t keep revisiting the things they are saying and checking them for accuracy. Many of us have published papers with errors — my most recent paper has an incorrect figure — and that’s after working on them carefully for weeks! One way that reporters can try to get things right is by quoting experts. Even then, there are problems with taking quotes out of context, or with making poor choices about what material to include or exclude, or, of course, with making a poor selection of experts. Yesterday, I was interviewed by an NPR reporter about the risks of breathing radon (a naturally occurring radioactive gas): who should test for it, how dangerous
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Introduction: I read this front-page New York Times article and was immediately suspicious. Here’s the story (from reporter Gina Kolata): Could exercise actually be bad for some healthy people? A well-known group of researchers, including one who helped write the scientific paper justifying national guidelines that promote exercise for all, say the answer may be a qualified yes. By analyzing data from six rigorous exercise studies involving 1,687 people, the group found that about 10 percent actually got worse on at least one of the measures related to heart disease: blood pressure and levels of insulin, HDL cholesterol or triglycerides. About 7 percent got worse on at least two measures. And the researchers say they do not know why. “It is bizarre,” said Claude Bouchard, lead author of the paper , published on Wednesday in the journal PLoS One . . . Dr. Michael Lauer, director of the Division of Cardiovascular Sciences at the National Heart, Lung, and Blood Institute, the lead federal
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Introduction: This post is by Phil. A little over three years ago I wrote a post about exercise and weight loss in which I described losing a fair amount of weight due to (I believe) an exercise regime, with no effort to change my diet; this contradicted the prediction of studies that had recently been released. The comment thread on that post is quite interesting: a lot of people had had similar experiences — losing weight, or keeping it off, with an exercise program that includes very short periods of exercise at maximal intensity — while other people expressed some skepticism about my claims. Some commenters said that I risked injury; others said it was too early to judge anything because my weight loss might not last. The people who predicted injury were right: running the curve during a 200m sprint a month or two after that post, I strained my Achilles tendon. Nothing really serious, but it did keep me off the track for a couple of months, and rather than go back to sprinting I switched t
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Introduction: OK, here’s something that is completely baffling me. I read this article by John Colapinto on the neuroscientist V. S. Ramachandran, who’s famous for his innovative treatment for “phantom limb” pain: His first subject was a young man who a decade earlier had crashed his motorcycle and torn from his spinal column the nerves supplying the left arm. After keeping the useless arm in a sling for a year, the man had the arm amputated above the elbow. Ever since, he had felt unremitting cramping in the phantom limb, as though it were immobilized in an awkward position. . . . Ramachandram positioned a twenty-inch-by-twenty-inch drugstore mirror . . . and told him to place his intact right arm on one side of the mirror and his stump on the other. He told the man to arrange the mirror so that the reflection created the illusion that his intact arm was the continuation of the amputated one. The Ramachandran asked the man to move his right and left arms . . . “Oh, my God!” the man began
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Introduction: By popular demand, here’s my R script for the time-use graphs : # The data a1 <- c(4.2,3.2,11.1,1.3,2.2,2.0) a2 <- c(3.9,3.2,10.0,0.8,3.1,3.1) a3 <- c(6.3,2.5,9.8,0.9,2.2,2.4) a4 <- c(4.4,3.1,9.8,0.8,3.3,2.7) a5 <- c(4.8,3.0,9.9,0.7,3.3,2.4) a6 <- c(4.0,3.4,10.5,0.7,3.3,2.1) a <- rbind(a1,a2,a3,a4,a5,a6) avg <- colMeans (a) avg.array <- t (array (avg, rev(dim(a)))) diff <- a - avg.array country.name <- c("France", "Germany", "Japan", "Britain", "USA", "Turkey") # The line plots par (mfrow=c(2,3), mar=c(4,4,2,.5), mgp=c(2,.7,0), tck=-.02, oma=c(3,0,4,0), bg="gray96", fg="gray30") for (i in 1:6){ plot (c(1,6), c(-1,1.7), xlab="", ylab="", xaxt="n", yaxt="n", bty="l", type="n") lines (1:6, diff[i,], col="blue") points (1:6, diff[i,], pch=19, col="black") if (i>3){ axis (1, c(1,3,5), c ("Work,\nstudy", "Eat,\nsleep", "Leisure"), mgp=c(2,1.5,0), tck=0, cex.axis=1.2) axis (1, c(2,4,6), c ("Unpaid\nwork", "Personal\nCare", "Other"), mgp=c(2,1.5,0),
2 0.95984679 151 andrew gelman stats-2010-07-16-Wanted: Probability distributions for rank orderings
Introduction: Dietrich Stoyan writes: I asked the IMS people for an expert in statistics of voting/elections and they wrote me your name. I am a statistician, but never worked in the field voting/elections. It was my son-in-law who asked me for statistical theories in that field. He posed in particular the following problem: The aim of the voting is to come to a ranking of c candidates. Every vote is a permutation of these c candidates. The problem is to have probability distributions in the set of all permutations of c elements. Are there theories for such distributions? I should be very grateful for a fast answer with hints to literature. (I confess that I do not know your books.) My reply: Rather than trying to model the ranks directly, I’d recommend modeling a latent continuous outcome which then implies a distribution on ranks, if the ranks are of interest. There are lots of distributions of c-dimensional continuous outcomes. In political science, the usual way to start is
Introduction: A tall thin young man came to my office today to talk about one of my current pet topics: stories and social science. I brought up Tom Wolfe and his goal of compressing an entire city into a single novel, and how this reminded me of the psychologists Kahneman and Tversky’s concept of “the law of small numbers,” the idea that we expect any small sample to replicate all the properties of the larger population that it represents. Strictly speaking, the law of small numbers is impossible—any small sample necessarily has its own unique features—but this is even more true if we consider network properties. The average American knows about 700 people (depending on how you define “know”) and this defines a social network over the population. Now suppose you look at a few hundred people and all their connections. This mini-network will almost necessarily look much much sparser than the national network, as we’re removing the connections to the people not in the sample. Now consider how
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Introduction: Steve Hsu, who started off this discussion, had some comments on my speculations on the personality of John von Neumann and others. Steve writes: I [Hsu] actually knew Feynman a bit when I was an undergrad, and found him to be very nice to students. Since then I have heard quite a few stories from people in theoretical physics which emphasize his nastier side, and I think in the end he was quite a complicated person like everyone else. There are a couple of pseudo-biographies of vN, but none as high quality as, e.g., Gleick’s book on Feynman or Hodges book about Turing. (Gleick studied physics as an undergrad at Harvard, and Hodges is a PhD in mathematical physics — pretty rare backgrounds for biographers!) For example, as mentioned on the comment thread to your post, Steve Heims wrote a book about both vN and Wiener (!), and Norman Macrae wrote a biography of vN. Both books are worth reading, but I think neither really do him justice. The breadth of vN’s work is just too m
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Introduction: Data analysis recipes: Fitting a model to data : We go through the many considerations involved in fitting a model to data, using as an example the fit of a straight line to a set of points in a two-dimensional plane. Standard weighted least-squares fitting is only appropriate when there is a dimension along which the data points have negligible uncertainties, and another along which all the uncertainties can be described by Gaussians of known variance; these conditions are rarely met in practice. We consider cases of general, heterogeneous, and arbitrarily covariant two-dimensional uncertainties, and situations in which there are bad data (large outliers), unknown uncertainties, and unknown but expected intrinsic scatter in the linear relationship being fit. Above all we emphasize the importance of having a “generative model” for the data, even an approximate one. Once there is a generative model, the subsequent fitting is non-arbitrary because the model permits direct computation
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