andrew_gelman_stats andrew_gelman_stats-2010 andrew_gelman_stats-2010-133 knowledge-graph by maker-knowledge-mining
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Introduction: I’m on an island in Maine for a few weeks (big shout out for North Haven!) This morning I picked up a copy of “Working Waterfront,” a newspaper that focuses on issues of coastal fishing communities. I came across an article about modeling “fish” populations — actually lobsters, I guess they’re considered “fish” for regulatory purposes. When I read it, I thought “wow, this article is really well-written, not dumbed down like articles in most newspapers.” I think it’s great that a small coastal newspaper carries reporting like this. (The online version has a few things that I don’t recall in the print version, too, so it’s even better). But in addition to being struck by finding such a good article in a small newspaper, I was struck by this: According to [University of Maine scientist Yong] Chen, there are four main areas where his model improved on the prior version. “We included the inshore trawl data from Maine and other state surveys, in addition to federal survey data; we h
sentIndex sentText sentNum sentScore
1 I’m on an island in Maine for a few weeks (big shout out for North Haven! [sent-1, score-0.202]
2 ) This morning I picked up a copy of “Working Waterfront,” a newspaper that focuses on issues of coastal fishing communities. [sent-2, score-0.791]
3 I came across an article about modeling “fish” populations — actually lobsters, I guess they’re considered “fish” for regulatory purposes. [sent-3, score-0.298]
4 When I read it, I thought “wow, this article is really well-written, not dumbed down like articles in most newspapers. [sent-4, score-0.118]
5 ” I think it’s great that a small coastal newspaper carries reporting like this. [sent-5, score-0.598]
6 (The online version has a few things that I don’t recall in the print version, too, so it’s even better). [sent-6, score-0.186]
7 But in addition to being struck by finding such a good article in a small newspaper, I was struck by this: According to [University of Maine scientist Yong] Chen, there are four main areas where his model improved on the prior version. [sent-7, score-0.676]
8 The phrase “virtual lobsters” is kinda nice, I think. [sent-9, score-0.154]
9 But check out the seemingly gratuitous mention of Bayesian statistics. [sent-10, score-0.174]
10 There’s just no way the intended audience for this article is expected to know what Bayesian statistics is — unlike “v-notching protection”, which is mentioned elsewhere but the article doesn’t bother to explain because, hey, everybody knows what v-notching protection is. [sent-11, score-0.916]
11 I’m not sure why Bayesian statistics is mentioned here. [sent-12, score-0.218]
12 Just to throw in some jargon in order to sound sophisticated? [sent-13, score-0.152]
13 Or is there some sense that people won’t know what Bayesian statistics is, but maybe they’ve heard that it’s a good thing? [sent-14, score-0.102]
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Introduction: I’m on an island in Maine for a few weeks (big shout out for North Haven!) This morning I picked up a copy of “Working Waterfront,” a newspaper that focuses on issues of coastal fishing communities. I came across an article about modeling “fish” populations — actually lobsters, I guess they’re considered “fish” for regulatory purposes. When I read it, I thought “wow, this article is really well-written, not dumbed down like articles in most newspapers.” I think it’s great that a small coastal newspaper carries reporting like this. (The online version has a few things that I don’t recall in the print version, too, so it’s even better). But in addition to being struck by finding such a good article in a small newspaper, I was struck by this: According to [University of Maine scientist Yong] Chen, there are four main areas where his model improved on the prior version. “We included the inshore trawl data from Maine and other state surveys, in addition to federal survey data; we h
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Introduction: John Cook noticed something : I [Cook] was looking at the preface of an old statistics book and read this: The Bayesian techniques occur at the end of each chapter; therefore they can be omitted if time does not permit their inclusion. This approach is typical. Many textbooks present frequentist statistics with a little Bayesian statistics at the end of each section or at the end of the book. There are a couple ways to look at that. One is simply that Bayesian methods are optional. They must not be that important or they’d get more space. The author even recommends dropping them if pressed for time. Another way to look at this is that Bayesian statistics must be simpler than frequentist statistics since the Bayesian approach to each task requires fewer pages. My reaction: Classical statistics is all about summarizing the data. Bayesian statistics is data + prior information. On those grounds alone, Bayes is more complicated, and it makes sense to do classical sta
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Introduction: Deborah Mayo collected some reactions to my recent article , Induction and Deduction in Bayesian Data Analysis. I’m pleased that that everybody (philosopher Mayo, applied statistician Stephen Senn, and theoretical statistician Larry Wasserman) is so positive about my article and that nobody’s defending the sort of hard-core inductivism that’s featured on the Bayesian inference wikipedia page. Here’s the Wikipedia definition, which I disagree with: Bayesian inference uses aspects of the scientific method, which involves collecting evidence that is meant to be consistent or inconsistent with a given hypothesis. As evidence accumulates, the degree of belief in a hypothesis ought to change. With enough evidence, it should become very high or very low. . . . Bayesian inference uses a numerical estimate of the degree of belief in a hypothesis before evidence has been observed and calculates a numerical estimate of the degree of belief in the hypothesis after evidence has been obse
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Introduction: I was talking with June Williamson and mentioned offhand that I’d seen something in the paper saying that if only we’d invested a few billion dollars in levees we would’ve saved zillions in economic damage from the flood. (A quick search also revealed this eerily prescient article from last month and, more recently, this online discussion.) June said, No, no, no: levees are not the way to go: Here and here are the articles on “soft infrastructure” for the New York-New Jersey Harbor I was mentioning, summarizing work that is more extensively published in two books, “Rising Currents” and “On the Water: Palisade Bay”: The hazards posed by climate change, sea level rise, and severe storm surges make this the time to transform our coastal cities through adaptive design. The conventional response to flooding, in recent history, has been hard engineering — fortifying the coastal infrastructure with seawalls and bulkheads to protect real estate at the expense of natural t
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Introduction: I’m on an island in Maine for a few weeks (big shout out for North Haven!) This morning I picked up a copy of “Working Waterfront,” a newspaper that focuses on issues of coastal fishing communities. I came across an article about modeling “fish” populations — actually lobsters, I guess they’re considered “fish” for regulatory purposes. When I read it, I thought “wow, this article is really well-written, not dumbed down like articles in most newspapers.” I think it’s great that a small coastal newspaper carries reporting like this. (The online version has a few things that I don’t recall in the print version, too, so it’s even better). But in addition to being struck by finding such a good article in a small newspaper, I was struck by this: According to [University of Maine scientist Yong] Chen, there are four main areas where his model improved on the prior version. “We included the inshore trawl data from Maine and other state surveys, in addition to federal survey data; we h
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