andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-1071 knowledge-graph by maker-knowledge-mining
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Introduction: One advantage of teaching statistics is that you don’t have to worry about any celebrities taking your class.
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Introduction: One advantage of teaching statistics is that you don’t have to worry about any celebrities taking your class.
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Introduction: X marks the spot . I’ll post the slides soon (not just for the students in my class; these should be helpful for anyone teaching Bayesian data analysis from our book ). But I don’t think you’ll get much from reading the slides alone; you’ll get more out of the book (or, of course, from taking the class).
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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
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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
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Introduction: My lecture for Greg’s class today (taken from chapters 5-6 of ARM). Also, after class we talked a bit more about formal modeling. If I have time I’ll post some of that discussion here.
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Introduction: Sharad Goel, Jake Hofman, and Sergei Vassilvitskii are teaching this awesome class on computational social science this semester in the applied math department at Columbia. Here’s the course info . You should take this course. These guys are amazing.
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
4 0.74898988 1008 andrew gelman stats-2011-11-13-Student project competition
Introduction: Yongtao Guan writes: I [Guan] recently began a collaboration with OpenIntro, a group of volunteers from around the country (Duke, Harvard, UCLA, and U.Miami) focused on contributing to improvements in introductory statistics education. They are an active group that has a lot of energy and neat ideas. On behalf of OpenIntro, I’d like to invite the students in your introductory statistics course to participate in a student project competition we are sponsoring this semester. Chris Barr (Assistant Professor at Harvard Biostatistics) and I are co-chairing the competition this Fall. We are hoping to highlight the excellent work that students do by hosting the two best projects from each class at OpenIntro.org and publishing the winners of a larger competition in a paperback volume with brief discussion about each project. Details of the competition can be found at openintro.org/stat/comp. We’ve tried to make the structure general so any class that already has a project integrated
Introduction: While visiting the education school at the University of Pennsylvania a couple months ago, I had a long conversation with Bob Boruch, a prominent researcher in the field of evidence-based education. We shared Fred Mosteller stories and talked about a lot of other things too. Boruch sent me an article about teaching randomized controlled trials to education students, which gave me the following idea which connects to my longstanding embarrassment (and subject of my next column on ethics, forthcoming in Chance magazine) about the lack of systematic measurement, sampling, or experimentation in our own teaching efforts. Anyway, here’s my idea for experimentation in statistics teaching, an idea that I think could work particularly well in classes with education students. Each class could, as part of the course, design an educational experiment to be performed on next year’s class. Easier said than done, I know, but perhaps ed school students would be particularly motivated to do t
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1 0.90864396 911 andrew gelman stats-2011-09-15-More data tools worth using from Google
Introduction: Speaking of open data and google tools, see this post from Revolution R: How to use a Google Spreadsheet as data in R .
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Introduction: Anthony Goldbloom writes: The Elo rating system is now in 47th position (team Elo Benchmark on the leaderboard). Team Intuition submitted using Microsoft’s Trueskill rating system – Intuition is in 38th position. And for the tourism forecasting competition, the best submission is doing better than the threshold for publication in the International Journal of Forecasting.
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Introduction: Oof! (if you’ll forgive my reference to bowling) What’s funny to me, though, is the phrase, “she’s not nearly as smart as she seems to think she is.” I mean, doesn’t that describe most people? (Link from here .) P.S. I hate to spell things out, Jeff, but . . . I hope you caught the Douglas Ginsburg reference!
Introduction: Here’s some psychology research that’s relevant to yesterday’s discussion on working-class voting. In a paper to appear in the journal Cognitive Science , Andrei Cimpian, Amanda Brandone, and Susan Gelman write: Generic statements (e.g., “Birds lay eggs”) express generalizations about categories. In this paper, we hypothesized that there is a paradoxical asymmetry at the core of generic meaning, such that these sentences have extremely strong implications but require little evidence to be judged true. Four experiments confirmed the hypothesized asymmetry: Participants interpreted novel generics such as “Lorches have purple feathers” as referring to nearly all lorches, but they judged the same novel generics to be true given a wide range of prevalence levels (e.g., even when only 10% or 30% of lorches had purple feathers). A second hypothesis, also confirmed by the results, was that novel generic sentences about dangerous or distinctive properties would be more acceptable than
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