andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-1023 knowledge-graph by maker-knowledge-mining
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Introduction: My article with the above title is appearing in the journal Teaching Statistics. Here’s the introduction: We can improve our teaching of statistical examples from books by collecting further data, reading cited articles and performing further data analysis. This should not come as a surprise, but what might be new is the realization of how close to the surface these research opportunities are: even influential and celebrated books can have examples where more can be learned with a small amount of additional effort. We discuss three examples that have arisen in our own teaching: an introductory textbook that motivated us to think more carefully about categorical and continuous variables; a book for the lay reader that misreported a study of menstruation and accidents; and a monograph on the foundations of probability that over interpreted statistically insignificant fluctuations in sex ratios. And here’s the conclusion: Individually, these examples are of little importance.
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1 My article with the above title is appearing in the journal Teaching Statistics. [sent-1, score-0.076]
2 Here’s the introduction: We can improve our teaching of statistical examples from books by collecting further data, reading cited articles and performing further data analysis. [sent-2, score-0.757]
3 This should not come as a surprise, but what might be new is the realization of how close to the surface these research opportunities are: even influential and celebrated books can have examples where more can be learned with a small amount of additional effort. [sent-3, score-0.703]
4 And here’s the conclusion: Individually, these examples are of little importance. [sent-5, score-0.279]
5 After all, one does not go to a statistics textbook to learn about handedness, menstruation, and sex ratios. [sent-6, score-0.404]
6 It is striking, however, that the very first examples I looked at in the Zeisel and von Mises books – the examples with interesting data patterns – collapsed upon further inspection. [sent-7, score-1.106]
7 In the Zeisel example, we went to the secondary source and found that his sketch was not actually a graph of any data, and that he in fact misinterpreted the results of the study. [sent-8, score-0.392]
8 In the von Mises example, we reanalysed the data and found his result to be not statistically significant, thus casting doubt on his already doubtful story about ethnic differences in sex ratios. [sent-9, score-0.855]
9 In the Utts and Heckard example, we were inspired to collect data on handedness and look at survey questions on religious attendance to find underlying continuous structures. [sent-10, score-0.683]
10 These are examples that I’ve encountered during the past twenty years of teaching. [sent-12, score-0.279]
11 Anything you read, you can check, for example this implausible (and, indeed, false) claim by a public health expert that “Consumption [of chicken] in the US has increased . [sent-14, score-0.274]
12 ) Textbooks are commonly written in an authoritative style, but that doesn’t mean everything in them is correct. [sent-19, score-0.175]
13 You can learn a lot by going back to the original source of the data, and even running the occasional chi-squared test of your own! [sent-20, score-0.199]
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same-blog 1 1.0000001 1023 andrew gelman stats-2011-11-22-Going Beyond the Book: Towards Critical Reading in Statistics Teaching
Introduction: My article with the above title is appearing in the journal Teaching Statistics. Here’s the introduction: We can improve our teaching of statistical examples from books by collecting further data, reading cited articles and performing further data analysis. This should not come as a surprise, but what might be new is the realization of how close to the surface these research opportunities are: even influential and celebrated books can have examples where more can be learned with a small amount of additional effort. We discuss three examples that have arisen in our own teaching: an introductory textbook that motivated us to think more carefully about categorical and continuous variables; a book for the lay reader that misreported a study of menstruation and accidents; and a monograph on the foundations of probability that over interpreted statistically insignificant fluctuations in sex ratios. And here’s the conclusion: Individually, these examples are of little importance.
2 0.16341922 621 andrew gelman stats-2011-03-20-Maybe a great idea in theory, didn’t work so well in practice
Introduction: I followed the link of commenter “Epanechnikov” to his blog, where I found, among other things, an uncritical discussion of Richard von Mises’s book, “Probability, Statistics and Truth.” The bad news is that, based on the evidence of his book, Mises didn’t seem to understand basic ideas of statistical significance. See here, Or at the very least, he was grossly overconfident (which can perhaps be seen from the brash title of his book). This is not the fault of “Epanechnikov,” but I just thought that people should be careful about taking too seriously the statistical philosophy of someone who didn’t think to do a chi-squared test when it was called for. (This is not a Bayesian/non-Bayesian thing; it’s just basic statistics.)
3 0.12815568 1013 andrew gelman stats-2011-11-16-My talk at Math for America on Saturday
Introduction: Here’s what I’ll talk about for 3 hours : Statistics—Inside and Outside the Classroom (1) Of Beauty, Sex, and Power: Statistical Challenges in the Estimation of Small Effects . A silly example of the frequencies of boy and girl babies leads us to some important research involving the meaning of statistical significance. (2) Mathematics, Statistics, and Political Science . We explore the differences between mathematical and statistical thinking, developing the ideas using examples from my own research in political science. (3) Statistics Teaching Activities . For twenty years I have been collecting class-participation demonstrations in statistics and probability. Here are some of my favorites.
Introduction: Someone sent me the following email: I am an environmental journalist writing an Environmental Science 101 textbook and I’m currently working on the section on hypothesis testing and statistical significance. I am searching for a story to make the importance of thinking statistically come alive for the students, ideally one from the environmental sciences. I’m looking for a time when an effect seemed huge to the naked eye, but wasn’t or a time when an error made an insignificant result look significant. Or maybe a story about how the media took an insignificant relationship and blew it out of proportion. Or maybe a story, like the one you told so well recently in Slate, about how you can find “significance” if you just keep throwing enough mud at the wall. It could be old or new, obscure or well known. The key thing, to make it work for the textbook, is that it have consequences—either implications outside of science, or high drama inside science. I pointed the textbook write
5 0.10267102 1605 andrew gelman stats-2012-12-04-Write This Book
Introduction: This post is by Phil Price. I’ve been preparing a review of a new statistics textbook aimed at students and practitioners in the “physical sciences,” as distinct from the social sciences and also distinct from people who intend to take more statistics courses. I figured that since it’s been years since I looked at an intro stats textbook, I should look at a few others and see how they differ from this one, so in addition to the book I’m reviewing I’ve looked at some other textbooks aimed at similar audiences: Milton and Arnold; Hines, Montgomery, Goldsman, and Borror; and a few others. I also looked at the table of contents of several more. There is a lot of overlap in the coverage of these books — they all have discussions of common discrete and continuous distributions, joint distributions, descriptive statistics, parameter estimation, hypothesis testing, linear regression, ANOVA, factorial experimental design, and a few other topics. I can see how, from a statisti
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17 0.08349511 1640 andrew gelman stats-2012-12-26-What do people do wrong? WSJ columnist is looking for examples!
18 0.082654744 583 andrew gelman stats-2011-02-21-An interesting assignment for statistical graphics
20 0.080147095 1074 andrew gelman stats-2011-12-20-Reading a research paper != agreeing with its claims
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Introduction: My article with the above title is appearing in the journal Teaching Statistics. Here’s the introduction: We can improve our teaching of statistical examples from books by collecting further data, reading cited articles and performing further data analysis. This should not come as a surprise, but what might be new is the realization of how close to the surface these research opportunities are: even influential and celebrated books can have examples where more can be learned with a small amount of additional effort. We discuss three examples that have arisen in our own teaching: an introductory textbook that motivated us to think more carefully about categorical and continuous variables; a book for the lay reader that misreported a study of menstruation and accidents; and a monograph on the foundations of probability that over interpreted statistically insignificant fluctuations in sex ratios. And here’s the conclusion: Individually, these examples are of little importance.
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Introduction: Russ Lyons points to an excellent article on statistical experimentation by Ron Kohavi, Alex Deng, Brian Frasca, Roger Longbotham, Toby Walker, Ya Xu, a group of software engineers (I presume) at Microsoft. Kohavi et al. write: Online controlled experiments are often utilized to make data-driven decisions at Amazon, Microsoft . . . deployment and mining of online controlled experiments at scale—thousands of experiments now—has taught us many lessons. The paper is well written and has excellent examples (unfortunately the substantive topics are unexciting things like clicks and revenue per user, but the general principles remain important). The ideas will be familiar to anyone with experience in practical statistics but don’t always make it into textbooks or courses, so I think many people could learn a lot from this article. I was disappointed that they didn’t cite much of the statistics literature— not even the classic Box, Hunter, and Hunter book on industrial experimentat
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Introduction: Mark Duckenfield writes: Some comments on statistics and “bad math”, that I think display a clear misunderstanding of statistics and surveys. http://www.armytimes.com/article/20130714/NEWS/307140016/Marine-officer-Scope-sex-assault-problem-exaggerated and the editorial to which it refers http://online.wsj.com/article/SB10001424127887323582904578484941173658754.html The original report is quite clear about weighting things, smaple sizes, etc. The apparent “clincher” argument in the editorial—that over 50% of unwanted sexual advances in the military are experienced by men—seems to confound several things. The first being, of course, that with only 14% of service members being female, it is quite likely by their broad definition that the 86% of service members who are men actually *do* comprise the majority of unwanted sexual attention even if the incidence is only a fraction of that of female service members. Of course, the general population, which is over 50% female would
4 0.73669875 1542 andrew gelman stats-2012-10-20-A statistical model for underdispersion
Introduction: We have lots of models for overdispersed count data but we rarely see underdispersed data. But now I know what example I’ll be giving when this next comes up in class. From a book review by Theo Tait: A number of shark species go in for oophagy, or uterine cannibalism. Sand tiger foetuses ‘eat each other in utero, acting out the harshest form of sibling rivalry imaginable’. Only two babies emerge, one from each of the mother shark’s uteruses: the survivors have eaten everything else. ‘A female sand tiger gives birth to a baby that’s already a metre long and an experienced killer,’ explains Demian Chapman, an expert on the subject. That’s what I call underdispersion. E(y)=2, var(y)=0. Take that, M. Poisson!
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Introduction: After reading Rachel and Cathy’s book , I wrote that “Statistics is the least important part of data science . . . I think it would be fair to consider statistics as a subset of data science. . . . it’s not the most important part of data science, or even close.” But then I received “Data Science for Business,” by Foster Provost and Tom Fawcett, in the mail. I might not have opened the book at all (as I’m hardly in the target audience) but for seeing a blurb by Chris Volinsky, a statistician whom I respect a lot. So I flipped through the book and it indeed looked pretty good. It moves slowly but that’s appropriate for an intro book. But what surprised me, given the book’s title and our recent discussion on the nature of data science, was that the book was 100% statistics! It had some math (for example, definitions of various distance measures), some simple algebra, some conceptual graphs such as ROC curve, some tables and graphs of low-dimensional data summaries—but almost
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Introduction: These (from Shaun Usher) are surprisingly good, especially since he appears to come up with new lists and letters pretty regularly. I suppose a lot of them get sent in from readers, but still. Here’s my favorite recent item, a letter sent to the Seattle Bureau of Prohibition in 1931: Dear Sir: My husband is in the habit of buying a quart of wiskey every other day from a Chinese bootlegger named Chin Waugh living at 317-16th near Alder street. We need this money for household expenses. Will you please have his place raided? He keeps a supply planted in the garden and a smaller quantity under the back steps for quick delivery. If you make the raid at 9:30 any morning you will be sure to get the goods and Chin also as he leaves the house at 10 o’clock and may clean up before he goes. Thanking you in advance, I remain yours truly, Mrs. Hillyer
Introduction: Paul Nee sends in this amusing item: MELA Sciences claimed success in a clinical trial of its experimental skin cancer detection device only by altering the statistical method used to analyze the data in violation of an agreement with U.S. regulators, charges an independent healthcare analyst in a report issued last week. . . The BER report, however, relies on its own analysis to suggest that MELA struck out with FDA because the agency’s medical device reviewers discovered the MELAFind pivotal study failed to reach statistical significance despite the company’s claims to the contrary. And now here’s where it gets interesting: MELA claims that a phase III study of MELAFind met its primary endpoint by detecting accurately 112 of 114 eligible melanomas for a “sensitivity” rate of 98%. The lower confidence bound of the sensitivity analysis was 95.1%, which met the FDA’s standard for statistical significance in the study spelled out in a binding agreement with MELA, the compa
3 0.96081036 1731 andrew gelman stats-2013-02-21-If a lottery is encouraging addictive gambling, don’t expand it!
Introduction: This story from Vivian Yee seems just horrible to me. First the background: Pronto Lotto’s real business takes place in the carpeted, hushed area where its most devoted customers watch video screens from a scattering of tall silver tables, hour after hour, day after day. The players — mostly men, about a dozen at any given time — come on their lunch breaks or after work to study the screens, which are programmed with the Quick Draw lottery game, and flash a new set of winning numbers every four minutes. They have helped make Pronto Lotto the top Quick Draw vendor in the state, selling $3.3 million worth of tickets last year, more than $1 million more than the second busiest location, a World Books shop in Penn Station. Some stay for just a few minutes. Others play for the length of a workday, repeatedly traversing the few yards between their seats and the cash register as they hand the next wager to a clerk with a dollar bill or two, and return to wait. “It’s like my job, 24
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Introduction: Sam Seaver writes: I’m a graduate student in computational biology, and I’m relatively new to advanced statistics, and am trying to teach myself how best to approach a problem I have. My dataset is a small sparse matrix of 150 cases and 70 predictors, it is sparse as in many zeros, not many ‘NA’s. Each case is a nutrient that is fed into an in silico organism, and its response is whether or not it stimulates growth, and each predictor is one of 70 different pathways that the nutrient may or may not belong to. Because all of the nutrients do not belong to all of the pathways, there are thus many zeros in my matrix. My goal is to be able to use the pathways themselves to predict whether or not a nutrient could stimulate growth, thus I wanted to compute regression coefficients for each pathway, with which I could apply to other nutrients for other species. There are quite a few singularities in the dataset (summary(glm) reports that 14 coefficients are not defined because of sin
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