andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-2044 knowledge-graph by maker-knowledge-mining
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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
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1 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. [sent-1, score-1.245]
2 I am searching for a story to make the importance of thinking statistically come alive for the students, ideally one from the environmental sciences. [sent-2, score-1.103]
3 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. [sent-3, score-0.542]
4 Or maybe a story about how the media took an insignificant relationship and blew it out of proportion. [sent-4, score-1.01]
5 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. [sent-5, score-0.606]
6 It could be old or new, obscure or well known. [sent-6, score-0.278]
7 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. [sent-7, score-0.546]
8 I pointed the textbook writer to this example , which seemed ideal: Although, as my correspondent pointed out to me, this example might be too complicated for students in an intro class. [sent-8, score-1.39]
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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
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Introduction: April Galyardt writes: I’m teaching my first graduate class this semester. It’s intro stats for graduate students in the college of education. Most of the students are first year PhD students. Though, there are a number of master’s students who are primarily in-service teachers. The difficulties with teaching an undergraduate intro stats course are still present, in that mathematical preparation and phobia vary widely across the class. I’ve been enjoying the class and the students, but I’d like your take on an issue I’ve been thinking about. How do I balance teaching the standard methods, like hypothesis testing, that these future researchers have to know because they are so standard, with discussing the problems with those methods (e.g. p-value as a measure of sample size , and the decline effect , not to mention multiple testing and other common mistakes). It feels a bit like saying “Ok here’s what everybody does, but really it’s broken” and then there’s not enough time to tal
Introduction: We’re doing a new thing here at the Applied Statistics Center, throwing monthly Friday afternoon mini-conferences in the Playroom (inspired by our successful miniconference on statistical consulting a couple years ago). This Friday (10 Sept), 1-5pm : Come join us this Friday, September 10th for an engaging interdisciplinary discussion of risk perception at the individual and societal level, and the role it plays in current environmental, social, and health policy debates. All are welcome! “Risk Perception in Environmental Decision-Making” Elke Weber, Columbia Business School “Cultural Cognition and the Problem of Science Communication” Dan Kahan, Yale Law School Discussants include: Michael Gerrard, Columbia Law School David Epstein, Department of Political Science, Columbia University Andrew Gelman, Department of Statistics, Columbia University
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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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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: 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
Introduction: Columbia College has for many years had a Core Curriculum, in which students read classics such as Plato (in translation) etc. A few years ago they created a Science core course. There was always some confusion about this idea: On one hand, how much would college freshmen really learn about science by reading the classic writings of Galileo, Laplace, Darwin, Einstein, etc.? And they certainly wouldn’t get much out by puzzling over the latest issues of Nature, Cell, and Physical Review Letters. On the other hand, what’s the point of having them read Dawkins, Gould, or even Brian Greene? These sorts of popularizations give you a sense of modern science (even to the extent of conveying some of the debates in these fields), but reading them might not give the same intellectual engagement that you’d get from wrestling with the Bible or Shakespeare. I have a different idea. What about structuring the entire course around computer programming and simulation? Start with a few weeks t
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Introduction: Alex Tabarrok writes : There is something special, magical, and “almost sacred” about the live teaching experience. I agree that this is true for teaching at its best but it’s also irrelevant. It’s even more true that there is something special, magical and almost sacred about the live musical experience. . . . Mark Edmundson makes the analogy between teaching and music explicit: Every memorable class is a bit like a jazz composition. Quite right but every non-memorable class is also a bit like a jazz composition, namely one that was expensive, took an hour to drive to (15 minutes just to find parking) and at the end of the day wasn’t very memorable. The correct conclusion to draw from the analogy between live teaching and live music is that at their best both are great but both are also costly and inefficient ways of delivering most teaching and most musical experiences. Excellent points (and Tabarrok has additional good points that I haven’t quoted). We’re not all
Introduction: Joe Blitzstein and Xiao-Li Meng write : An e ffectively designed examination process goes far beyond revealing students’ knowledge or skills. It also serves as a great teaching and learning tool, incentivizing the students to think more deeply and to connect the dots at a higher level. This extends throughout the entire process: pre-exam preparation, the exam itself, and the post-exam period (the aftermath or, more appropriately, afterstat of the exam). As in the publication process, the first submission is essential but still just one piece in the dialogue. Viewing the entire exam process as an extended dialogue between students and faculty, we discuss ideas for making this dialogue induce more inspiration than perspiration, and thereby making it a memorable deep-learning triumph rather than a wish-to-forget test-taking trauma. We illustrate such a dialogue through a recently introduced course in the Harvard Statistics Department, Stat 399: Problem Solving in Statistics, and tw
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