andrew_gelman_stats andrew_gelman_stats-2010 andrew_gelman_stats-2010-259 knowledge-graph by maker-knowledge-mining
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Introduction: Just in time for the new semester: This time I’m sticking with the plan : 1. Don’t open a message until I’m ready to deal with it. 2. Don’t store anything–anything–in the inbox. 3. Put to-do items in the (physical) bookje rather than the (computer) “desktop.” 4. Never read email before 4pm. (This is the one rule I have been following. 5. Only one email session per day. (I’ll have to see how this one works.)
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3 Put to-do items in the (physical) bookje rather than the (computer) “desktop. [sent-6, score-0.305]
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Introduction: Just in time for the new semester: This time I’m sticking with the plan : 1. Don’t open a message until I’m ready to deal with it. 2. Don’t store anything–anything–in the inbox. 3. Put to-do items in the (physical) bookje rather than the (computer) “desktop.” 4. Never read email before 4pm. (This is the one rule I have been following. 5. Only one email session per day. (I’ll have to see how this one works.)
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Introduction: A few days ago I reported on the spam email that I received from two business school professors (one at Columbia)! As noted on the blog, I sent an email directly to the study’s authors at the time of reading the email, but they have yet to respond. This surprises me a bit. Certainly if 6300 faculty each have time to respond to one email on this study, the two faculty have time to respond to 6300 email replies, no? I was actually polite enough to respond to both of their emails! If I do hear back, I’ll let youall know! P.S. Paul Basken interviewed me briefly for a story in the Chronicle of Higher Education on the now-notorious spam email study. Basken’s article is reasonable–he points out that (a) the study irritated a lot of people, but (b) is ultimately no big deal. One interesting thing about the article is that, although some people felt that the spam email study was ethical, nobody came forth with an argument that the study was actually worth doing. P.P.S. In
4 0.13366903 18 andrew gelman stats-2010-05-06-$63,000 worth of abusive research . . . or just a really stupid waste of time?
Introduction: As someone who relies strongly on survey research, it’s good for me to be reminded that some surveys are useful, some are useless, but one thing they almost all have in common is . . . they waste the respondents’ time. I thought of this after receiving the following email, which I shall reproduce here. My own comments appear after. Recently, you received an email from a student asking for 10 minutes of your time to discuss your Ph.D. program (the body of the email appears below). We are emailing you today to debrief you on the actual purpose of that email, as it was part of a research study. We sincerely hope our study did not cause you any disruption and we apologize if you were at all inconvenienced. Our hope is that this letter will provide a sufficient explanation of the purpose and design of our study to alleviate any concerns you may have about your involvement. We want to thank you for your time and for reading further if you are interested in understanding why you rece
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Introduction: Just in time for the new semester: This time I’m sticking with the plan : 1. Don’t open a message until I’m ready to deal with it. 2. Don’t store anything–anything–in the inbox. 3. Put to-do items in the (physical) bookje rather than the (computer) “desktop.” 4. Never read email before 4pm. (This is the one rule I have been following. 5. Only one email session per day. (I’ll have to see how this one works.)
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3 0.75740957 1573 andrew gelman stats-2012-11-11-Incredibly strange spam
Introduction: Unsolicited (of course) in the email the other day: Just wanted to touch base with you to see if you needed any quotes on Parking lot lighting or Garage Lighting? (Induction, LED, Canopy etc…) We help retrofit 1000′s of garages around the country. Let me know your specs and ill send you a quote in 24 hours. ** Owner Emergency Lights Co. Ill indeed. . . .
Introduction: This is hilarious ( link from a completely deadpan Tyler Cowen). I’d call it “unintentionally hilarious” but I’m pretty sure that rms knew this was funny when he was writing it. It’s sort of like when you write a top 10 list—it’s hard to resist getting silly and going over the top. It’s only near the end that we get to the bit about the parrots. All joking aside, the most interesting part of the email was this: I [rms] have to spend 6 to 8 hours *every day* doing my usual work, which is responding to email about the GNU Project and the Free Software Movement. I’d wondered for awhile what is it that Richard Stallman actually does, that is how does he spend his time (aside from giving lectures to promote his ideas and pay the bills). Emailing –> Blogging I too spend a lot of time on email, but a few years ago I consciously tried to shift a bunch of my email exchanges to the blog. I found that I was sending out a lot of information to an audience of one, information
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Introduction: This one totally faked me out at first. It was an email from “Nick Bagnall” that began: Dear Dr. Gelman, I made contact last year regarding your work in the CMG: Reconstructing Climate from Tree Ring Data project. We are about to start producing the 2014 edition and I wanted to discuss this with you as we still remain keen to feature your work. Research Media are producing a special publication in February of 2014, within this report we will be working with a small selected number of PI’s with a focus on geosciences, atmospheric and geospace sciences and earth Sciences.. At this point, I’m thinking: Hmmm, I don’t remember this guy, is this some sort of collaborative project that I’d forgotten about? The message then continues: The publication is called International Innovation . . . Huh? This doesn’t sound so good. The email then goes on with some very long lists, and then finally the kicker: The total cost for each article produced in this report is fixed a
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Introduction: Justin Kinney writes: I wanted to let you know that the critique Mickey Atwal and I wrote regarding equitability and the maximal information coefficient has just been published . We discussed this paper last year, under the heading, Too many MC’s not enough MIC’s, or What principles should govern attempts to summarize bivariate associations in large multivariate datasets? Kinney and Atwal’s paper is interesting, with my only criticism being that in some places they seem to aim for what might not be possible. For example, they write that “mutual information is already widely believed to quantify dependencies without bias for relationships of one type or another,” which seems a bit vague to me. And later they write, “How to compute such an estimate that does not bias the resulting mutual information value remains an open problem,” which seems to me to miss the point in that unbiased statistical estimates are not generally possible and indeed are often not desirable. Their
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Introduction: Interesting discussion from David Gorski (which I found via this link from Joseph Delaney). I don’t have anything really to add to this discussion except to note the value of this sort of anecdote in a statistics discussion. It’s only n=1 and adds almost nothing to the literature on the effectiveness of various treatments, but a story like this can help focus one’s thoughts on the decision problems.
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Introduction: Sharad had a survey sampling question: We’re trying to use mechanical turk to conduct some surveys, and have quickly discovered that turkers tend to be quite young. We’d really like a representative sample of the U.S., or at the least be able to recruit a diverse enough sample from turk that we can post-stratify to adjust the estimates. The approach we ended up taking is to pay turkers a small amount to answer a couple of screening questions (age & sex), and then probabilistically recruit individuals to complete the full survey (for more money) based on the estimated turk population parameters and our desired target distribution. We use rejection sampling, so the end result is that individuals who are invited to take the full survey look as if they came from a representative sample, at least in terms of age and sex. I’m wondering whether this sort of technique—a two step design in which participants are first screened and then probabilistically selected to mimic a target distributio
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Introduction: Nick Firoozye writes: I had a question about BMA [Bayesian model averaging] and model combinations in general, and direct it to you since they are a basic form of hierarchical model, albeit in the simplest of forms. I wanted to ask what the underlying assumptions are that could lead to BMA improving on a larger model. I know model combination is a topic of interest in the (frequentist) econometrics community (e.g., Bates & Granger, http://www.jstor.org/discover/10.2307/3008764?uid=3738032&uid;=2&uid;=4&sid;=21101948653381) but at the time it was considered a bit of a puzzle. Perhaps small models combined outperform a big model due to standard errors, insufficient data, etc. But I haven’t seen much in way of Bayesian justification. In simplest terms, you might have a joint density P(Y,theta_1,theta_2) from which you could use the two marginals P(Y,theta_1) and P(Y,theta_2) to derive two separate forecasts. A BMA-er would do a weighted average of the two forecast densities, having p
Introduction: Our discussion on data visualization continues. One one side are three statisticians–Antony Unwin, Kaiser Fung, and myself. We have been writing about the different goals served by information visualization and statistical graphics. On the other side are graphics experts (sorry for the imprecision, I don’t know exactly what these people do in their day jobs or how they are trained, and I don’t want to mislabel them) such as Robert Kosara and Jen Lowe , who seem a bit annoyed at how my colleagues and myself seem to follow the Tufte strategy of criticizing what we don’t understand. And on the third side are many (most?) academic statisticians, econometricians, etc., who don’t understand or respect graphs and seem to think of visualization as a toy that is unrelated to serious science or statistics. I’m not so interested in the third group right now–I tried to communicate with them in my big articles from 2003 and 2004 )–but I am concerned that our dialogue with the graphic
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