andrew_gelman_stats andrew_gelman_stats-2012 andrew_gelman_stats-2012-1264 knowledge-graph by maker-knowledge-mining
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Introduction: I was talking with education researcher Bob Boruch about my frustrations in teaching, the idea that as statisticians we tell people to do formal experimentation but in our own teaching practice we typically just try different things without even measuring outcomes, let alone performing any formal evaluation. Boruch showed me this article with Alan Ruby about learning from failure. Unfortunately I’ve forgotten all my other thoughts from our conversation but I’m posting the article here.
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Introduction: I was talking with education researcher Bob Boruch about my frustrations in teaching, the idea that as statisticians we tell people to do formal experimentation but in our own teaching practice we typically just try different things without even measuring outcomes, let alone performing any formal evaluation. Boruch showed me this article with Alan Ruby about learning from failure. Unfortunately I’ve forgotten all my other thoughts from our conversation but I’m posting the article here.
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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Introduction: Commenter Wonks Anonymous writes : After the recent EconNobel announcement I decided to check Dimensional’s Fama-French blog to see if it had much new content recently, and while it was dissapointingly sparse it did have an interesting bit where Fama linked to the best advice he’d ever gotten , from his statistics professor Harry Roberts: With formal statistics, you say something — a hypothesis — and then you test it. Harry always said that your criterion should be not whether or not you can reject or accept the hypothesis, but what you can learn from the data. The best thing you can do is use the data to enhance your description of the world. I responded: That’s a great quote. Except that I disagree with what Fama says about “formal statistics.” Or, should I say, he has an old-fashioned view of formal statistics. (See this paper by X and me for some discussion of old-fashioned views.) Nowadays, lots of formal statistics is all about what you can learn from the data, no
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Introduction: As a statistician, I was trained to think of randomized experimentation as representing the gold standard of knowledge in the social sciences, and, despite having seen occasional arguments to the contrary, I still hold that view, expressed pithily by Box, Hunter, and Hunter (1978) that “To find out what happens when you change something, it is necessary to change it.” At the same time, in my capacity as a social scientist, I’ve published many applied research papers, almost none of which have used experimental data. In the present article, I’ll address the following questions: 1. Why do I agree with the consensus characterization of randomized experimentation as a gold standard? 2. Given point 1 above, why does almost all my research use observational data? In confronting these issues, we must consider some general issues in the strategy of social science research. We also take from the psychology methods literature a more nuanced perspective that considers several differen
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Introduction: I was talking with education researcher Bob Boruch about my frustrations in teaching, the idea that as statisticians we tell people to do formal experimentation but in our own teaching practice we typically just try different things without even measuring outcomes, let alone performing any formal evaluation. Boruch showed me this article with Alan Ruby about learning from failure. Unfortunately I’ve forgotten all my other thoughts from our conversation but I’m posting the article here.
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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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: 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
Introduction: For “humanity, devotion to truth and inspiring leadership” at Columbia College. Reading Jenny’s remarks (“my hugest and most helpful pool of colleagues was to be found not among the ranks of my fellow faculty but in the classroom. . . . we shared a sense of the excitement of the enterprise on which we were all embarked”) reminds me of the comment Seth made once, that the usual goal of university teaching is to make the students into carbon copies of the instructor, and that he found it to me much better to make use of the students’ unique strengths. This can’t always be true–for example, in learning to speak a foreign language, I just want to be able to do it, and my own experiences in other domains is not so relevant. But for a worldly subject such as literature or statistics or political science, then, yes, I do think it would be good for students to get involved and use their own knowledge and experiences. One other statement of Jenny’s caught my eye. She wrote: I [Je
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Introduction: Sciencedaily has posted an article titled Apes Unwilling to Gamble When Odds Are Uncertain : The apes readily distinguished between the different probabilities of winning: they gambled a lot when there was a 100 percent chance, less when there was a 50 percent chance, and only rarely when there was no chance In some trials, however, the experimenter didn’t remove a lid from the bowl, so the apes couldn’t assess the likelihood of winning a banana The odds from the covered bowl were identical to those from the risky option: a 50 percent chance of getting the much sought-after banana. But apes of both species were less likely to choose this ambiguous option. Like humans, they showed “ambiguity aversion” — preferring to gamble more when they knew the odds than when they didn’t. Given some of the other differences between chimps and bonobos, Hare and Rosati had expected to find the bonobos to be more averse to ambiguity, but that didn’t turn out to be the case. Thanks to Sta
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Introduction: Hear me interviewed on the topic here . P.S. The interview was fine but I don’t agree with everything on the linked website. For example, this bit: Global warming is not the first case of a widespread fear based on incomplete knowledge turned out to be false or at least greatly exaggerated. Global warming has many of the characteristics of a popular delusion, an irrational fear or cause that is embraced by millions of people because, well, it is believed by millions of people! All right, then.
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Introduction: Burak Bayramli writes: In this paper by Sunjin Ahn, Anoop Korattikara, and Max Welling and this paper by Welling and Yee Whye The, there are some arguments on big data and the use of MCMC. Both papers have suggested improvements to speed up MCMC computations. I was wondering what your thoughts were, especially on this paragraph: When a dataset has a billion data-cases (as is not uncommon these days) MCMC algorithms will not even have generated a single (burn-in) sample when a clever learning algorithm based on stochastic gradients may already be making fairly good predictions. In fact, the intriguing results of Bottou and Bousquet (2008) seem to indicate that in terms of “number of bits learned per unit of computation”, an algorithm as simple as stochastic gradient descent is almost optimally efficient. We therefore argue that for Bayesian methods to remain useful in an age when the datasets grow at an exponential rate, they need to embrace the ideas of the stochastic optimiz
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