andrew_gelman_stats andrew_gelman_stats-2014 andrew_gelman_stats-2014-2354 knowledge-graph by maker-knowledge-mining
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Introduction: Just in case you didn’t check Retraction Watch yet today , Carolyn Johnson reports: The committee painstakingly reconstructed the process of data analysis and determined that Hauser had changed values, causing the result to be statistically significant, an important criterion showing that findings are probably not due to chance. As the man said : His resignation is a serious loss for Harvard, and given the nature of the attack on him, for science generally. As a statistician, I don’t mind if someone is attacked because of cheating with data. Johnson concludes her news article in a pleasantly balanced way: The committee said it carefully considered Hauser’s allegation that people in his laboratory conspired against him, due to academic rivalry and disgruntlement, but did not find evidence to support the idea. The committee also acknowledged that many of Hauser’s overall findings about the cognitive abilities of animals may stand. His results that showed that animals
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1 As the man said : His resignation is a serious loss for Harvard, and given the nature of the attack on him, for science generally. [sent-2, score-0.198]
2 As a statistician, I don’t mind if someone is attacked because of cheating with data. [sent-3, score-0.155]
3 Johnson concludes her news article in a pleasantly balanced way: The committee said it carefully considered Hauser’s allegation that people in his laboratory conspired against him, due to academic rivalry and disgruntlement, but did not find evidence to support the idea. [sent-4, score-1.106]
4 The committee also acknowledged that many of Hauser’s overall findings about the cognitive abilities of animals may stand. [sent-5, score-0.956]
5 His results that showed that animals may have some of the same cognitive abilities as people have been important for the field. [sent-6, score-0.507]
6 “Skepticism above all toward the veracity of one’s own hypotheses is, of course, an essential virtue for scientists,” the committee wrote, “and one that must be modeled for the benefit of trainees. [sent-8, score-0.577]
7 Given the title of this post, I should probably clarify that I do not blame statistical significance for Hauser’s offenses. [sent-11, score-0.068]
8 Any statistical procedure of any sort will be destroyed if you start monkeying around with the data. [sent-12, score-0.197]
9 In all seriousness, I wonder if Hauser feels more relaxed now that he’s been caught and these conclusions are out in the open. [sent-16, score-0.088]
10 He can spend more time doing the theorizing he loves, rather than the experimental work that freaked him out so much. [sent-17, score-0.197]
11 Shifting to this new career couldn’t have been so easy for him, but now that it’s happened, maybe he’s reached more of a state of calm. [sent-18, score-0.139]
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same-blog 1 1.0000001 2354 andrew gelman stats-2014-05-30-Mmm, statistical significance . . . Evilicious!
Introduction: Just in case you didn’t check Retraction Watch yet today , Carolyn Johnson reports: The committee painstakingly reconstructed the process of data analysis and determined that Hauser had changed values, causing the result to be statistically significant, an important criterion showing that findings are probably not due to chance. As the man said : His resignation is a serious loss for Harvard, and given the nature of the attack on him, for science generally. As a statistician, I don’t mind if someone is attacked because of cheating with data. Johnson concludes her news article in a pleasantly balanced way: The committee said it carefully considered Hauser’s allegation that people in his laboratory conspired against him, due to academic rivalry and disgruntlement, but did not find evidence to support the idea. The committee also acknowledged that many of Hauser’s overall findings about the cognitive abilities of animals may stand. His results that showed that animals
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Introduction: The other day, a friend told me that when he saw me blogging on Noam Chomsky, he was surprised not to see any mention of disgraced primatologist Marc Hauser. I was like, whaaaaaa? I had no idea these two had any connection. In fact, though, they wrote papers together. This made me wonder what Chomsky thought of Hauser’s data scandal. I googled *marc hauser noam chomsky* and the first item that came up was this, from July 2011, reported by Tom Bartlett: I [Bartlett] asked Chomsky for his comment on the Hauser resignation and he e-mailed the following: Mark Hauser is a fine scientist with an outstanding record of accomplishment. His resignation is a serious loss for Harvard, and given the nature of the attack on him, for science generally. Chomsky is a mentor of Hauser so I can’t fault Chomsky for defending the guy. But why couldn’t he have stuck with something more general, something like, “I respect and admire Mark Hauser and am not aware of any improprieties in his w
Introduction: An offhand mention in this recent post motivated me to google Evilicious. And this is what I found: From the prologue: The idea I [Hauser] develop is that evildoers are made in much the same way that addicts are made. Both processes start with unsatisfied desires. Whether it is a taste for violence or a taste for alcohol, drugs, food, or gambling, individuals develop cravings but find the desired experience less and less rewarding, a separation between desire and reward that leads to excess. To justify the excess, the psychology of desire recruits the psychology of denial, enabling individuals to immerse themselves in a new reality that feels right. Interesting. This could be an excellent book. I imagine that the author has a lot of insight into this psychological state of immersing oneself in a new reality that feels right. Or maybe we all do this. In any case, thinking about extreme evil (what Hauser calls “gratuitous cruelty”) is helpful in developing a sense of pers
Introduction: As regular readers of this blog are aware, I am fascinated by academic and scientific cheating and the excuses people give for it. Bruno Frey and colleagues published a single article (with only minor variants) in five different major journals, and these articles did not cite each other. And there have been several other cases of his self-plagiarism (see this review from Olaf Storbeck). I do not mind the general practice of repeating oneself for different audiences—in the social sciences, we call this Arrow’s Theorem —but in this case Frey seems to have gone a bit too far. Blogger Economic Logic has looked into this and concluded that this sort of common practice is standard in “the context of the German(-speaking) academic environment,” and what sets Frey apart is not his self-plagiarism or even his brazenness but rather his practice of doing it in high-visibility journals. Economic Logic writes that “[Frey's] contribution is pedagogical, he found a good and interesting
5 0.1048429 902 andrew gelman stats-2011-09-12-The importance of style in academic writing
Introduction: In my comments on academic cheating , I briefly discussed the question of how some of these papers could’ve been published in the first place, given that they tend to be of low quality. (It’s rare that people plagiarize the good stuff, and, when they do—for example when a senior scholar takes credit for a junior researcher’s contributions without giving proper credit—there’s not always a paper trail, and there can be legitimate differences of opinion about the relative contributions of the participants.) Anyway, to get back to the cases at hand: how did these rulebreakers get published in the first place? The question here is not how did they get away with cheating but how is it that top journals were publishing mediocre research? In the case of the profs who falsified data (Diederik Stapel) or did not follow scientific protocol (Mark Hauser), the answer is clear: By cheating, they were able to get the sort of too-good-to-be-true results which, if they were true, would be
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Introduction: Just in case you didn’t check Retraction Watch yet today , Carolyn Johnson reports: The committee painstakingly reconstructed the process of data analysis and determined that Hauser had changed values, causing the result to be statistically significant, an important criterion showing that findings are probably not due to chance. As the man said : His resignation is a serious loss for Harvard, and given the nature of the attack on him, for science generally. As a statistician, I don’t mind if someone is attacked because of cheating with data. Johnson concludes her news article in a pleasantly balanced way: The committee said it carefully considered Hauser’s allegation that people in his laboratory conspired against him, due to academic rivalry and disgruntlement, but did not find evidence to support the idea. The committee also acknowledged that many of Hauser’s overall findings about the cognitive abilities of animals may stand. His results that showed that animals
Introduction: As regular readers of this blog are aware, I am fascinated by academic and scientific cheating and the excuses people give for it. Bruno Frey and colleagues published a single article (with only minor variants) in five different major journals, and these articles did not cite each other. And there have been several other cases of his self-plagiarism (see this review from Olaf Storbeck). I do not mind the general practice of repeating oneself for different audiences—in the social sciences, we call this Arrow’s Theorem —but in this case Frey seems to have gone a bit too far. Blogger Economic Logic has looked into this and concluded that this sort of common practice is standard in “the context of the German(-speaking) academic environment,” and what sets Frey apart is not his self-plagiarism or even his brazenness but rather his practice of doing it in high-visibility journals. Economic Logic writes that “[Frey's] contribution is pedagogical, he found a good and interesting
Introduction: Someone points me to this report from Tilburg University on disgraced psychology researcher Diederik Stapel. The reports includes bits like this: When the fraud was first discovered, limiting the harm it caused for the victims was a matter of urgency. This was particularly the case for Mr Stapel’s former PhD students and postdoctoral researchers . . . However, the Committees were of the opinion that the main bulk of the work had not yet even started. . . . Journal publications can often leave traces that reach far into and even beyond scientific disciplines. The self-cleansing character of science calls for fraudulent publications to be withdrawn and no longer to proliferate within the literature. In addition, based on their initial impressions, the Committees believed that there were other serious issues within Mr Stapel’s publications . . . This brought into the spotlight a research culture in which this sloppy science, alongside out-and-out fraud, was able to remain undetected
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Introduction: Mark Palko writes : I can understand the appeal of the cutting edge. The new stuff is sexier. It gets people’s attention. The trouble is, those cutting edge studies often collapse under scrutiny. Some can’t be replicated. Others prove to be not that important. Confirmation, on the other hand, is not sexy. It doesn’t drive traffic. It’s harder to fit into a paragraph. In a way, though, it’s more interesting because it has a high likelihood of being true and fills in the gaps in big, important questions. The interaction between the ideas is usually the interesting part. In this particular example, Palko is telling the story of a journalist who reports a finding as new when it is essentially a replication of decades-old work. Palko’s point is not that there’s anything wrong with replication but rather that the journalist seems to feel that it is necessary to report the idea as new and cutting-edge, even if it falls within a long tradition. (Also, Palko is not claiming that this
5 0.74744403 1449 andrew gelman stats-2012-08-08-Gregor Mendel’s suspicious data
Introduction: Howard Wainer points me to a thoughtful discussion by Moti Nissani on “Psychological, Historical, and Ethical Reflections on the Mendelian Paradox.” The paradox, as Nissani defines it, is that Mendel’s data seem in many cases too good to be true, yet Mendel had a reputation for probity and it seems doubtful that he had a Mark-Hauser-style attitude toward reporting scientific data. Nissani writes: Taken together, the situation seems paradoxical. On the one hand, we have evidence that “the data of most, if not all, of the experiments have been falsified so as to agree closely with Mendel’s expectations.” We also have good reasons to believe that Mendel encountered linkage but failed to report it and that he may have taken the somewhat unusual step of having his scientific records destroyed shortly after his death. On the other hand, everything else we know about him/in addition to his undisputed genius/suggests a man of unimpeachable integrity, fine observational powers, and a pa
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Introduction: Just in case you didn’t check Retraction Watch yet today , Carolyn Johnson reports: The committee painstakingly reconstructed the process of data analysis and determined that Hauser had changed values, causing the result to be statistically significant, an important criterion showing that findings are probably not due to chance. As the man said : His resignation is a serious loss for Harvard, and given the nature of the attack on him, for science generally. As a statistician, I don’t mind if someone is attacked because of cheating with data. Johnson concludes her news article in a pleasantly balanced way: The committee said it carefully considered Hauser’s allegation that people in his laboratory conspired against him, due to academic rivalry and disgruntlement, but did not find evidence to support the idea. The committee also acknowledged that many of Hauser’s overall findings about the cognitive abilities of animals may stand. His results that showed that animals
Introduction: Mike Spagat sent me an email with the above heading, referring to this paper by Leontine Alkema and Jin Rou New, which begins: National estimates of the under-5 mortality rate (U5MR) are used to track progress in reducing child mortality and to evaluate countries’ performance related to United Nations Millennium Development Goal 4, which calls for a reduction in the U5MR by two-thirds between 1990 and 2015. However, for the great majority of developing countries without well-functioning vital registration systems, estimating levels and trends in child mortality is challenging, not only because of limited data availability but also because of issues with data quality. Global U5MR estimates are often constructed without accounting for potential biases in data series, which may lead to inaccurate point estimates and/or credible intervals. We describe a Bayesian penalized B-spline regression model for assessing levels and trends in the U5MR for all countries in the world, whereby bi
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Introduction: Some thoughts from Christian , including this bit: We need to consider separately 1. R’s brilliant library 2. R’s not-so-brilliant language and/or interpreter. I don’t know that R’s library is so brilliant as all that–if necessary, I don’t think it would be hard to reprogram the important packages in a new language. I would say, though, that the problems with R are not just in the technical details of the language. I think the culture of R has some problems too. As I’ve written before, R functions used to be lean and mean, and now they’re full of exception-handling and calls to other packages. R functions are spaghetti-like messes of connections in which I keep expecting to run into syntax like “GOTO 120.” I learned about these problems a couple years ago when writing bayesglm(), which is a simple adaptation of glm(). But glm(), and its workhorse, glm.fit(), are a mess: They’re about 10 lines of functioning code, plus about 20 lines of necessary front-end, plus a cou
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Introduction: Kaiser Fung shares this graph from Ritchie King: Kaiser writes: What they did right: - Did not put the data on a map - Ordered the countries by the most recent data point rather than alphabetically - Scale labels are found only on outer edge of the chart area, rather than one set per panel - Only used three labels for the 11 years on the plot - Did not overdo the vertical scale either The nicest feature was the XL scale applied only to South Korea. This destroys the small-multiples principle but draws attention to the top left corner, where the designer wants our eyes to go. I would have used smaller fonts throughout. I agree with all of Kaiser’s comments. I could even add a few more, like using light gray for the backgrounds and a bright blue for the lines, spacing the graphs well, using full country names rather than three-letter abbreviations. There are so many standard mistakes that go into default data displays that it is refreshing to see a simple graph done
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