Blog Archives

Questions about “Too Good to Be True”

September 2, 2014
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Greg Won writes: I manage a team tasked with, among other things, analyzing data on Air Traffic operations to identify factors that may be associated with elevated risk. I think its fair to characterize our work as “data mining” (e.g., using rule induction, Bayesian, and statistical methods). One of my colleagues sent me a link […] The post Questions about “Too Good to Be True” appeared first on Statistical Modeling,…

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Bad Statistics: Ignore or Call Out?

September 1, 2014
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Evelyn Lamb adds to the conversation that Jeff Leek and I had a few months ago. It’s a topic that’s worth returning to, in light of our continuing discussions regarding the crisis of criticism in science. The post Bad Statistics: Ignore or...

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On deck this week

September 1, 2014
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Mon: Bad Statistics: Ignore or Call Out? Tues: Questions about “Too Good to Be True” Wed: I disagree with Alan Turing and Daniel Kahneman regarding the strength of statistical evidence Thurs: Why isn’t replication required before publication in top journals? Fri: Confirmationist and falsificationist paradigms of science Sat: How does inference for next year’s data […] The post On deck this week appeared first on Statistical Modeling, Causal Inference, and…

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On deck this month

August 30, 2014
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Bad Statistics: Ignore or Call Out? Questions about “Too Good to Be True” I disagree with Alan Turing and Daniel Kahneman regarding the strength of statistical evidence Why isn’t replication required before publication in top journals? Confirmationist and falsificationist paradigms of science How does inference for next year’s data differ from inference for unobserved data […] The post On deck this month appeared first on Statistical Modeling, Causal Inference, and…

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Avoiding model selection in Bayesian social research

August 29, 2014
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One of my favorites, from 1995. Don Rubin and I argue with Adrian Raftery. Here’s how we begin: Raftery’s paper addresses two important problems in the statistical analysis of social science data: (1) choosing an appropriate model when so much data are available that standard P-values reject all parsimonious models; and (2) making estimates and […] The post Avoiding model selection in Bayesian social research appeared first on Statistical Modeling,…

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When we talk about the “file drawer,” let’s not assume that an experiment can easily be characterized as producing strong, mixed, or weak results

August 28, 2014
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Neil Malhotra: I thought you might be interested in our paper [the paper is by Annie Franco, Neil Malhotra, and Gabor Simonovits, and the link is to a news article by Jeffrey Mervis], forthcoming in Science, about publication bias in the social sciences given your interest and work on research transparency. Basic summary: We examined […] The post When we talk about the “file drawer,” let’s not assume that an…

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Pre-election survey methodology: details from nine polling organizations, 1988 and 1992

August 28, 2014
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This one from 1995 (with D. Stephen Voss and Gary King) was fun. For our “Why are American Presidential election campaign polls so variable when votes are so predictable?” project a few years earlier, Gary and I had analyzed individual-level survey responses from 60 pre-election polls that had been conducted by several different polling organizations. […] The post Pre-election survey methodology: details from nine polling organizations, 1988 and 1992 appeared…

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Discussion of “A probabilistic model for the spatial distribution of party support in multiparty elections”

August 27, 2014
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From 1994. I don’t have much to say about this one. The paper I was discussing (by Samuel Merrill) had already been accepted by the journal—I might even have been a referee, in which case the associate editor had decided to accept the paper over my objections—and the editor gave me the opportunity to publish […] The post Discussion of “A probabilistic model for the spatial distribution of party support…

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Dave Blei course on Foundations of Graphical Models

August 26, 2014
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Dave Blei course on Foundations of Graphical Models

Dave Blei writes: This course is cross listed in Computer Science and Statistics at Columbia University. It is a PhD level course about applied probabilistic modeling. Loosely, it will be similar to this course. Students should have some background in probability, college-level mathematics (calculus, linear algebra), and be comfortable with computer programming. The course is […] The post Dave Blei course on Foundations of Graphical Models appeared first on Statistical…

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Review of “Forecasting Elections”

August 26, 2014
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From 1993. The topic of election forecasting sure gets a lot more attention than it used to! Here are some quotes from my review of that book by Michael Lewis-Beck and Tom Rice: Political scientists are aware that most voters are consistent in their preferences, and one can make a good guess just looking at […] The post Review of “Forecasting Elections” appeared first on Statistical Modeling, Causal Inference, and…

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