Updating fast and slow

October 29, 2016
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Updating fast and slow

Paul Campos pointed me to this post from a couple of days ago in which he wrote: I think it’s fair to say that right now the consensus among elite observers across the ideological spectrum . . . is that the presidential race is over because Donald Trump has no chance of winning — or […] The post Updating fast and slow appeared first on Statistical Modeling, Causal Inference, and…

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No evidence shark attacks swing elections

October 29, 2016
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No evidence shark attacks swing elections

Anthony Fowler and Andy Hall write: We reassess Achen and Bartels’ (2002, 2016) prominent claim that shark attacks influence presidential elections, and we find that the evidence is, at best, inconclusive. First, we assemble data on every fatal shark attack in U.S. history and county-level returns from every presidential election between 1872 and 2012, and […] The post No evidence shark attacks swing elections appeared first on Statistical Modeling, Causal…

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Conflicts of interest

October 29, 2016
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Paul Alper writes: The following involves Novartis so it may be of interest to you. This Washington Post article headline says: Extending anti-estrogen therapy to 10 years reduces breast-cancer recurrence, new cancers Nevertheless, However, women who were treated with the drug for a total 10 years didn’t live longer than those who were given a […] The post Conflicts of interest appeared first on Statistical Modeling, Causal Inference, and Social…

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“The Warriors suck”: A Bayesian exploration

October 29, 2016
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“The Warriors suck”:  A Bayesian exploration

A basketball fan of my close acquaintance woke up Wednesday morning and, upon learning the outcome of the first games of the NBA season, announced that “The Warriors suck.” Can we answer this question? To put it more precisely, how much information is supplied by that first-game-of-season blowout? Speaking Bayesianly, who much should we adjust […] The post “The Warriors suck”: A Bayesian exploration appeared first on Statistical Modeling, Causal…

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A quick exploration of the ReporteRs package

October 28, 2016
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The package ReporteRs has been getting some play on the interwebs this week, though it’s actually been around for a while. The nice thing about this package is that it allows writing Word and PowerPoint documents in an OS-independent fashion unlike some earlier packages. It also allows the editing of documents by using bookmarks within […]

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Happiness of liberals and conservatives in different countries

October 28, 2016
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Jay Livingston writes: I recall that you have used my post showing that the happiness of conservatives is related to who’s in power. So you might be interested in this multi-nation study showing the same thing: Generally, conservatives are happier than non-conservatives. However, “that is mostly the case in conservative countries. In liberal countries, they […] The post Happiness of liberals and conservatives in different countries appeared first on Statistical…

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The idol worship of objective data is damaging our discipline

October 28, 2016
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In class last week, I discussed this New York Times article with the students. One of the claims in the article is that the U.S. News ranking of colleges is under threat by newcomers whose rankings are more relevant because they more directly measure outcomes such as earnings of graduates. This specific claim in the article makes me head hurt: "If nothing else, earnings are objective and, as the database…

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Not So Standard Deviations Episode 25 – How Exactly Do You Pronounce SQL?

October 28, 2016
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Hilary and I go through the overflowing mailbag to respond to listener questions! Topics include causal inference in trend modeling, regression model selection, using SQL, and data science certification. If you have questions you’d like us to answer...

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“Generic and consistent confidence and credible regions”

October 27, 2016
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“Generic and consistent confidence and credible regions”

Christian Bartels sends along this paper, which begins: A generic, consistent, efficient and exact method is proposed for set selection. The method is generic in that its definition and implementation uses only the likelihood function. The method is consistent in that the same criterion is used to select confidence and credible sets making the two […] The post “Generic and consistent confidence and credible regions” appeared first on Statistical Modeling,…

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Yes, despite what you may have heard, you can easily fit hierarchical mixture models in Stan

October 26, 2016
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There was some confusion on the Stan list that I wanted to clear up, having to do with fitting mixture models. Someone quoted this from John Kruschke’s book, Doing Bayesian Data Analysis: The lack of discrete parameters in Stan means that we cannot do model comparison as a hierarchical model with an indexical parameter at […] The post Yes, despite what you may have heard, you can easily fit hierarchical…

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The problems are everywhere, once you know to look

October 26, 2016
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The problems are everywhere, once you know to look

Josh Miller writes: My friend and colleague Joachim Vosgerau (at Bocconi) sent me some papers from PNAS and they are right in your wheelhouse. Higher social class people behave more unethically. I can certainly vouch for the jerky behavior of people that drive BMWs and Mercedes in Italy (similar to Study 1&2 in Piff et […] The post The problems are everywhere, once you know to look appeared first on…

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Create patterns of missing data

October 26, 2016
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Create patterns of missing data

When simulating data or testing algorithms, it is useful to be able to generate patterns of missing data. This article shows how to generate random and systematic patterns of missing values. In other words, this article shows how to replace nonmissing data with missing data. Generate a random pattern of […] The post Create patterns of missing data appeared first on The DO Loop.

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VIS 2016 – Tuesday

October 26, 2016
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VIS 2016 – Tuesday

The official opening of the main conference was today, Tuesday. The conference is now in full swing until Friday. Opening Attendance at the conference is flat – Terry Yoo gave no precise numbers, but at least it's not shrinking. I figure they don't want to release precise numbers in the hope that there's just a […]

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Colorless green ideas tweet furiously

October 26, 2016
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Colorless green ideas tweet furiously

Nadia Hassan writes: Justin Wolfers and Nate Silver got into a colorful fight on twitter. Nate has 2 forecasts. Nate is doing a polls-only forecast in addition to a “traditional” one that discounts poll leads and builds in fundamentals. Wolfers noted that the 538 polls-only model had Clinton at a higher chance of winning on […] The post Colorless green ideas tweet furiously appeared first on Statistical Modeling, Causal Inference,…

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Are Datasets the New Server Rooms?

October 26, 2016
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Josh Nussbaum has an interesting post over at Medium about whether massive datasets are the new server rooms of tech business. The analogy comes from the “old days” where in order to start an Internet business, you had to buy racks and servers, re...

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Should researchers be correcting for multiple tests, even when they themselves did not run the tests, but all of the tests were run on the same data?

October 25, 2016
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A graduate student, named Caitlin Ducate, in my frequentist statistics class asks:In Criminal Justice, it's common to use large data sets like the Uniform Crime Report (UCR) or versions of the National Longitudinal Survey (NLS) because the nature of ...

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Socks, skeets, space aliens

October 25, 2016
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Socks, skeets, space aliens

In my Bayesian statistics class this semester, I asked students to invent new Bayes theorem problems, with the following criteria:1) A good Bayes's theorem problem should pose an interesting question that seems hard to solve directly, but2) It should be easier to solve with Bayes's theorem than without it, and3) It should have some element of surprise, or at least a non-obvious outcome.Several years ago I posted some of my…

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How not to analyze noisy data: A case study

October 25, 2016
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How not to analyze noisy data:  A case study

I was reading Jenny Davidson’s blog and came upon this note on an autobiography of the eccentric (but aren’t we all?) biologist Robert Trivers. This motivated me, not to read Trivers’s book, but to do some googling which led me to this paper from Plos-One, “Revisiting a sample of U.S. billionaires: How sample selection and […] The post How not to analyze noisy data: A case study appeared first on…

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VIS 2016 – Sunday, Monday: BELIV and Being Contrarian

October 25, 2016
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VIS 2016 – Sunday, Monday: BELIV and Being Contrarian

The early part of IEEE VIS 2016 is already behind us. This includes many workshops, tutorials, as well as the Doctoral Colloquium. It has been an interesting three days (considering Saturday here as well). This posting is less a report as a number of observations from a several discussions and talks. Doctoral Colloquium The reason I’m including […]

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Why Journalists need to understand statistics – Sensational Listener article about midwifery risks

October 25, 2016
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Why Journalists need to understand statistics – Sensational Listener article about midwifery risks

The recent article in the Listener highlights again the need for all citizens to  be statistically literate. In particular I believe statistical literacy should be a compulsory part of all journalists’ training. I have written before about this. I was happy … Continue reading →

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Ptolemaic inference

October 24, 2016
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Ptolemaic inference

OK, we’ve been seeing this a lot recently. A psychology study gets published, with a key idea that at first seems wacky but, upon closer reflection, could very well be true! Examples: – That “dentist named Dennis” paper suggesting that people pick where they live and what job to take based on their names. – […] The post Ptolemaic inference appeared first on Statistical Modeling, Causal Inference, and Social Science.

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And Yet It Moves: Gravitational Waves

October 24, 2016
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And Yet It Moves: Gravitational Waves

"The moment he was set at liberty, he looked up to the sky and down to the ground, and, stamping with his foot, in a contemplative mood, said, Eppur si muove [And yet it moves], meaning the earth."1Giuseppe Baretti, on Galileo GalileiGalileo ...

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And Yet It Moves: Gravitational Waves

October 24, 2016
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And Yet It Moves: Gravitational Waves

"The moment he was set at liberty, he looked up to the sky and down to the ground, and, stamping with his foot, in a contemplative mood, said, Eppur si muove [And yet it moves], meaning the earth."1Giuseppe Baretti, on Galileo GalileiGalileo ...

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