andrew_gelman_stats andrew_gelman_stats-2010 andrew_gelman_stats-2010-238 knowledge-graph by maker-knowledge-mining
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Introduction: Kaiser writes thoughtfully about the costs, benefits, and incentives for different policy recommendation options regarding a recent water crisis. Good stuff: it’s solid “freakonomics”–and I mean this in positive way: a mix of economic and statistical analysis, with assumptions stated clearly. Kaiser writes: Using the framework from Chapter 4, we should think about the incentives facing the Mass. Water Resources Authority: A false positive error (people asked to throw out water when water is clean) means people stop drinking tap water temporarily, perhaps switching to bottled water, and the officials claim victory when no one falls sick, and businesses that produce bottled water experience a jump in sales. It is also very difficult to prove a “false positive” when people have stopped drinking the water. So this type of error is easy to hide behind. A false negative error (people told it’s safe to drink water when water is polluted) becomes apparent when someone falls sick
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1 Kaiser writes thoughtfully about the costs, benefits, and incentives for different policy recommendation options regarding a recent water crisis. [sent-1, score-0.886]
2 Good stuff: it’s solid “freakonomics”–and I mean this in positive way: a mix of economic and statistical analysis, with assumptions stated clearly. [sent-2, score-0.149]
3 Kaiser writes: Using the framework from Chapter 4, we should think about the incentives facing the Mass. [sent-3, score-0.148]
4 It is also very difficult to prove a “false positive” when people have stopped drinking the water. [sent-5, score-0.172]
5 We think the risk is low but if it happens, the false negative error creates a public relations nightmare. [sent-8, score-0.374]
6 I [Kaiser] think this goes a long way to explaining why government officials behave the way they do. [sent-9, score-0.175]
7 This applies also to the FDA and CDC in terms of foodborne diseases (a subject of Chapter 2), and to the NTSB in terms of car recalls. [sent-10, score-0.182]
8 In the case of food or product recalls, being overly conservative leads to massive economic losses and waste as food or products are thrown out, almost all of them good. [sent-12, score-0.408]
9 The Environmental Protection Agency had a recommendation that every homeowner in the country measure their radon levels, and that anyone with a measurement higher than 4 picoCuries per liter get their house remediated. [sent-14, score-0.387]
10 We recommended a much more targeted strategy which we estimated could save the same number of lives at much less cost. [sent-15, score-0.059]
11 Radon is a natural hazard, and so there’s no radon manufacturer’s association pushing to minimize its risks. [sent-18, score-0.283]
12 If anything, polluters like to focus on radon as it takes the hook off them for other problems. [sent-19, score-0.283]
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same-blog 1 1.0000001 238 andrew gelman stats-2010-08-27-No radon lobby
Introduction: Kaiser writes thoughtfully about the costs, benefits, and incentives for different policy recommendation options regarding a recent water crisis. Good stuff: it’s solid “freakonomics”–and I mean this in positive way: a mix of economic and statistical analysis, with assumptions stated clearly. Kaiser writes: Using the framework from Chapter 4, we should think about the incentives facing the Mass. Water Resources Authority: A false positive error (people asked to throw out water when water is clean) means people stop drinking tap water temporarily, perhaps switching to bottled water, and the officials claim victory when no one falls sick, and businesses that produce bottled water experience a jump in sales. It is also very difficult to prove a “false positive” when people have stopped drinking the water. So this type of error is easy to hide behind. A false negative error (people told it’s safe to drink water when water is polluted) becomes apparent when someone falls sick
2 0.309798 179 andrew gelman stats-2010-08-03-An Olympic size swimming pool full of lithium water
Introduction: As part of his continuing plan to sap etc etc., Aleks pointed me to an article by Max Miller reporting on a recommendation from Jacob Appel: Adding trace amounts of lithium to the drinking water could limit suicides. . . . Communities with higher than average amounts of lithium in their drinking water had significantly lower suicide rates than communities with lower levels. Regions of Texas with lower lithium concentrations had an average suicide rate of 14.2 per 100,000 people, whereas those areas with naturally higher lithium levels had a dramatically lower suicide rate of 8.7 per 100,000. The highest levels in Texas (150 micrograms of lithium per liter of water) are only a thousandth of the minimum pharmaceutical dose, and have no known deleterious effects. I don’t know anything about this and am offering no judgment on it; I’m just passing it on. The research studies are here and here . I am skeptical, though, about this part of the argument: We are not talking a
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Introduction: Bernard Phiri writes: I am relatively new to glm models, anyhow, I am currently using your book “Data analysis using regression and multilevel/hierarchical models” (pages 109-115). I am using a Poisson GLM model to analyse an aerial census dataset of wild herbivores on a ranch in Kenya. In my analysis I have the following variables: 1. Outcome variable: count of wild herbivores sighted at a given location 2. Explanatory variable1: vegetation type i.e. type of vegetation of the grid in which animals were sighted (the ranch is divided into 1x1km grids) 3. Explanatory variable2: animal species e.g. eland, elephant, zebra etc 4. Exposure: proximity to water i.e. distance (km) to the nearest water point My questions are as follows: 1. Am I correct to include proximity to water point as an offset? I notice that in the example in your book the offset is a count, does this matter? 2. By including proximity to water in the model as an exposure am I correct to interpret th
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Introduction: Visual Economics shows statistics on average food consumption in America: My brief feedback is that water is confounded with these results. They should have subtracted water content from the weight of all dietary items, as it inflates the proportion of milk, vegetable and fruit items that contain more water. They did that for soda (which is represented as sugar/corn syrup), amplifying the inconsistency. Time Magazine had a beautiful gallery that visualizes diets around the world in a more appealing way.
5 0.13403234 514 andrew gelman stats-2011-01-13-News coverage of statistical issues…how did I do?
Introduction: This post is by Phil Price. A reporter once told me that the worst-kept secret of journalism is that every story has errors. And it’s true that just about every time I know about something first-hand, the news stories about it have some mistakes. Reporters aren’t subject-matter experts, they have limited time, and they generally can’t keep revisiting the things they are saying and checking them for accuracy. Many of us have published papers with errors — my most recent paper has an incorrect figure — and that’s after working on them carefully for weeks! One way that reporters can try to get things right is by quoting experts. Even then, there are problems with taking quotes out of context, or with making poor choices about what material to include or exclude, or, of course, with making a poor selection of experts. Yesterday, I was interviewed by an NPR reporter about the risks of breathing radon (a naturally occurring radioactive gas): who should test for it, how dangerous
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Introduction: Kaiser writes thoughtfully about the costs, benefits, and incentives for different policy recommendation options regarding a recent water crisis. Good stuff: it’s solid “freakonomics”–and I mean this in positive way: a mix of economic and statistical analysis, with assumptions stated clearly. Kaiser writes: Using the framework from Chapter 4, we should think about the incentives facing the Mass. Water Resources Authority: A false positive error (people asked to throw out water when water is clean) means people stop drinking tap water temporarily, perhaps switching to bottled water, and the officials claim victory when no one falls sick, and businesses that produce bottled water experience a jump in sales. It is also very difficult to prove a “false positive” when people have stopped drinking the water. So this type of error is easy to hide behind. A false negative error (people told it’s safe to drink water when water is polluted) becomes apparent when someone falls sick
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Introduction: On the side of less regulation is Alex Tabarrok in “Our DNA, Our Selves”: At the same time that the NSA is secretly and illegally obtaining information about Americans the FDA is making it illegal for Americans to obtain information about themselves. In a warning letter the FDA has told Anne Wojcicki, The Most Daring CEO In America, that she “must immediately discontinue” selling 23andMe’s Personal Genome Service . . . Alex clarifies: I am not offended by all regulation of genetic tests. Indeed, genetic tests are already regulated. . . . the Clinical Laboratory Improvement Amendments (CLIA) . . . requires all labs, including the labs used by 23andMe, to be inspected for quality control, record keeping and the qualifications of their personnel. . . . What the FDA wants to do is categorically different. The FDA wants to regulate genetic tests as a high-risk medical device . . . the FDA wants to judge . . . the clinical validity, whether particular identified alleles are cau
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Introduction: I keep encountering the word “Groupon”–I think it’s some sort of pets.com-style commercial endeavor where people can buy coupons? I don’t really care, and I’ve avoided googling the word out of a general animosity toward our society’s current glorification of get-rich-quick schemes. (As you can tell, I’m still bitter about that whole stock market thing.) Anyway, even without knowing what Groupon actually is, I enjoyed this blog by Kaiser Fung in which he tries to work out some of its economic consequences. He connects the statistical notion of counterfactuals to the concept of opportunity cost from economics. The comments are interesting too.
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Introduction: Tyler Cowen links to a “scary comparison” that claims that “a one-parent family of three making $14,500 a year (minimum wage) has more disposable income than a family making $60,000 a year.” Kaiser Fung looks into this comparison in more detail. As Kaiser puts it: If we concede that the middle-income person would end up with less disposable income than the lower-income person, then we’d expect that the middle-income people will take lower-paying jobs so as to increase their disposable income. But I have not seen reports of such reverse social mobility. Theory needs to fit reality. This hole in the theory needs to be covered. This argument sounds convincing at first, but I’m not completely sure it’s right. I’d just like to expand on one of Kaiser’s other points, which is that people don’t usually have a choice between a minimum-wage job and a $60,000 job. Also, my impression is that higher-paying jobs are more pleasant than lower-paying jobs. Let me put it another w
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Introduction: Kaiser Fung was ahead of the curve on Lance Armstrong: The media has gotten the statistics totally backwards. On the one hand, they faithfully report the colorful stories of athletes who fail drug tests pleading their innocence. (I have written about the Spanish cyclist Alberto Contador here.) On the other hand, they unquestioningly report athletes who claim “hundreds of negative tests” prove their honesty. Putting these two together implies that the media believes that negative test results are highly reliable while positive test results are unreliable. The reality is just the opposite. When an athlete tests positive, it’s almost sure that he/she has doped. Sure, most of the clean athletes will test negative but what is often missed is that the majority of dopers will also test negative. We don’t need to do any computation to see that this is true. In most major sports competitions, the proportion of tests declared positive is typically below 1%. If you believe that the pr
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Introduction: Alexander at GiveWell writes : The Disease Control Priorities in Developing Countries (DCP2), a major report funded by the Gates Foundation . . . provides an estimate of $3.41 per disability-adjusted life-year (DALY) for the cost-effectiveness of soil-transmitted-helminth (STH) treatment, implying that STH treatment is one of the most cost-effective interventions for global health. In investigating this figure, we have corresponded, over a period of months, with six scholars who had been directly or indirectly involved in the production of the estimate. Eventually, we were able to obtain the spreadsheet that was used to generate the $3.41/DALY estimate. That spreadsheet contains five separate errors that, when corrected, shift the estimated cost effectiveness of deworming from $3.41 to $326.43. [I think they mean to say $300 -- ed.] We came to this conclusion a year after learning that the DCP2’s published cost-effectiveness estimate for schistosomiasis treatment – another kind of
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Introduction: I received the following (unsolicited) email: Hello, *** LLC, a ***-based market research company, has a financial client who is interested in speaking with a statistician who has done research in the field of Alzheimer’s Disease and preferably familiar with the SOLA and BAPI trials. We offer an honorarium of $200 for a 30 minute telephone interview. Please advise us if you have an employment or consulting agreement with any organization or operate professionally pursuant to an organization’s code of conduct or employee manual that may control activities by you outside of your regular present and former employment, such as participating in this consulting project for MedPanel. If there are such contracts or other documents that do apply to you, please forward MedPanel a copy of each such document asap as we are obligated to review such documents to determine if you are permitted to participate as a consultant for MedPanel on a project with this particular client. If you are
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Introduction: Kaiser writes thoughtfully about the costs, benefits, and incentives for different policy recommendation options regarding a recent water crisis. Good stuff: it’s solid “freakonomics”–and I mean this in positive way: a mix of economic and statistical analysis, with assumptions stated clearly. Kaiser writes: Using the framework from Chapter 4, we should think about the incentives facing the Mass. Water Resources Authority: A false positive error (people asked to throw out water when water is clean) means people stop drinking tap water temporarily, perhaps switching to bottled water, and the officials claim victory when no one falls sick, and businesses that produce bottled water experience a jump in sales. It is also very difficult to prove a “false positive” when people have stopped drinking the water. So this type of error is easy to hide behind. A false negative error (people told it’s safe to drink water when water is polluted) becomes apparent when someone falls sick
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Introduction: Mike Zyphur writes: Kaggle.com has launched a competition to determine what’s an effect and what’s a cause. They’ve got correlated variables, they’re deprived of context, and you’re asked to determine the causal order. $5,000 prizes. I followed the link and the example they gave didn’t make much sense to me (the two variables were temperature and altitude of cities in Germany, and they said that altitude causes temperature). It has the feeling to me of one of those weird standardized tests we used to see sometimes in school, where there’s no real correct answer so the goal is to figure out what the test-writer wanted you to say. Nonetheless, this might be of interest, so I’m passing it along to you.
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