andrew_gelman_stats andrew_gelman_stats-2013 andrew_gelman_stats-2013-2153 knowledge-graph by maker-knowledge-mining
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Introduction: Govind Manian points me to this online textbook by Alex Reinhart. It’s hard for me to evaluate because I am so close to the material. But on first glance it looks pretty reasonable to me.
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same-blog 1 1.0 2153 andrew gelman stats-2013-12-29-“Statistics Done Wrong”
Introduction: Govind Manian points me to this online textbook by Alex Reinhart. It’s hard for me to evaluate because I am so close to the material. But on first glance it looks pretty reasonable to me.
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Introduction: Govind Manian writes: I wanted to pass along a fragment from Lichtenberg’s Waste Books — which I am finding to be great stone soup — that reminded me of God is in Every Leaf : To the wise man nothing is great and nothing small…I believe he could write treatises on keyholes that sounded as weighty as a jus naturae and would be just as instructive. As the few adepts in such things well know, universal morality is to be found in little everyday penny-events just as much as in great ones. There is so much goodness and ingenuity in a raindrop that an apothecary wouldn’t let it go for less than half-a-crown… (Notebook B, 33)
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Introduction: Alex points me to this paper by Lirong Xia. With its model of independent identically distributed votes, the paper is a bit theoretical for my taste, but maybe it will interest some of you.
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Introduction: This looks cool.
Introduction: Someone sent me the following email: I am an environmental journalist writing an Environmental Science 101 textbook and I’m currently working on the section on hypothesis testing and statistical significance. I am searching for a story to make the importance of thinking statistically come alive for the students, ideally one from the environmental sciences. I’m looking for a time when an effect seemed huge to the naked eye, but wasn’t or a time when an error made an insignificant result look significant. Or maybe a story about how the media took an insignificant relationship and blew it out of proportion. Or maybe a story, like the one you told so well recently in Slate, about how you can find “significance” if you just keep throwing enough mud at the wall. It could be old or new, obscure or well known. The key thing, to make it work for the textbook, is that it have consequences—either implications outside of science, or high drama inside science. I pointed the textbook write
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same-blog 1 0.96325326 2153 andrew gelman stats-2013-12-29-“Statistics Done Wrong”
Introduction: Govind Manian points me to this online textbook by Alex Reinhart. It’s hard for me to evaluate because I am so close to the material. But on first glance it looks pretty reasonable to me.
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Introduction: Abby points us to a spare but cool visualization . I don’t like the curvy connect-the-dots line, but my main suggested improvement would be a closer link to the map . Showing median income on census tracts along subway lines is cool, but ultimately it’s a clever gimmick that pulls me in and makes me curious about what the map looks like. (And, thanks to google, the map was easy to find.)
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Introduction: Govind Manian writes: I wanted to pass along a fragment from Lichtenberg’s Waste Books — which I am finding to be great stone soup — that reminded me of God is in Every Leaf : To the wise man nothing is great and nothing small…I believe he could write treatises on keyholes that sounded as weighty as a jus naturae and would be just as instructive. As the few adepts in such things well know, universal morality is to be found in little everyday penny-events just as much as in great ones. There is so much goodness and ingenuity in a raindrop that an apothecary wouldn’t let it go for less than half-a-crown… (Notebook B, 33)
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Introduction: This looks cool.
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Introduction: I Paid a Bribe by Janaagraha, a Bangalore based not-for-profit, harnesses the collective energy of citizens and asks them to report on the nature, number, pattern, types, location, frequency and values of corruption activities. These reports would be used to argue for improving governance systems and procedures, tightening law enforcement and regulation and thereby reduce the scope for corruption. Here’s a presentation of data from the application: Transparency International could make something like this much more widely available around the world . While awareness is good, follow-up is even better. For example, it’s known that New York’s subway signal inspections were being falsified . Signal inspections are pretty serious stuff, as failures lead to disasters , such as the one in Washington. Nothing much happened after: the person responsible (making $163k a year) was merely reassigned .
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Introduction: Govind Manian points me to this online textbook by Alex Reinhart. It’s hard for me to evaluate because I am so close to the material. But on first glance it looks pretty reasonable to me.
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Introduction: “Ich glaube, dass die Wahrscheinlichkeitsrechnung das richtige Werkzeug zum Lösen solcher Probleme ist”, sagt Andrew Gelman , Statistikprofessor von der Columbia-Universität in New York. Wie oft aber derart knifflige Aufgaben im realen Leben auftauchen, könne er nicht sagen. Was fast schon beruhigend klingt. OK, fine.
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Introduction: These (from Shaun Usher) are surprisingly good, especially since he appears to come up with new lists and letters pretty regularly. I suppose a lot of them get sent in from readers, but still. Here’s my favorite recent item, a letter sent to the Seattle Bureau of Prohibition in 1931: Dear Sir: My husband is in the habit of buying a quart of wiskey every other day from a Chinese bootlegger named Chin Waugh living at 317-16th near Alder street. We need this money for household expenses. Will you please have his place raided? He keeps a supply planted in the garden and a smaller quantity under the back steps for quick delivery. If you make the raid at 9:30 any morning you will be sure to get the goods and Chin also as he leaves the house at 10 o’clock and may clean up before he goes. Thanking you in advance, I remain yours truly, Mrs. Hillyer
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Introduction: Paul Nee sends in this amusing item: MELA Sciences claimed success in a clinical trial of its experimental skin cancer detection device only by altering the statistical method used to analyze the data in violation of an agreement with U.S. regulators, charges an independent healthcare analyst in a report issued last week. . . The BER report, however, relies on its own analysis to suggest that MELA struck out with FDA because the agency’s medical device reviewers discovered the MELAFind pivotal study failed to reach statistical significance despite the company’s claims to the contrary. And now here’s where it gets interesting: MELA claims that a phase III study of MELAFind met its primary endpoint by detecting accurately 112 of 114 eligible melanomas for a “sensitivity” rate of 98%. The lower confidence bound of the sensitivity analysis was 95.1%, which met the FDA’s standard for statistical significance in the study spelled out in a binding agreement with MELA, the compa
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