Blog Archives

Measuring Bias in Published Work

July 31, 2013
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Measuring Bias in Published Work

In a series of previous posts, I’ve spent some time looking at the idea that the review and publication process in political science—and specifically, the requirement that a result must be statistically significant in order to be scientifically notable or publishable—produces a very misleading scientific literature. In short, published studies of some relationship will tend

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p-values are (possibly biased) estimates of the probability that the null hypothesis is true

March 31, 2013
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p-values are (possibly biased) estimates of the probability that the null hypothesis is true

Last week, I posted about statisticians’ constant battle against the belief that the p-value associated (for example) with a regression coefficient is equal to the probability that the null hypothesis is true, for a null hypothesis that beta is zero or negative. I argued that (despite our long pedagogical practice) there are, in fact, many

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How to make a scientific result disappear

February 27, 2013
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How to make a scientific result disappear

Nathan Danneman (a co-author and one of my graduate students from Emory) recently sent me a New Yorker article from 2010 about the “decline effect,” the tendency for initially promising scientific results to get smaller upon replication. Wikipedia can summarize the phenomenon as well as I can: In his article, Lehrer gives several examples where

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Proposed techniques for communicating the amount of information contained in a statistical result

February 5, 2013
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Proposed techniques for communicating the amount of information contained in a statistical result

A couple of weeks ago, I posted about how much we can expect to learn about the state of the world on the basis of a statistical significance test. One way of framing this question is: if we’re trying to come to scientific conclusions on the basis of statistical results, how much can we update

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How much can we learn from an empirical result? A Bayesian approach to power analysis and the implications for pre-registration.

January 18, 2013
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How much can we learn from an empirical result? A Bayesian approach to power analysis and the implications for pre-registration.

Just like a lot of political science departments, here at Rice a group of faculty and students meet each week to discuss new research in political methodology. This week, we read a new symposium in Political Analysis about the pre-registration of studies in political science. To briefly summarize, several researchers argued that political scientists should

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Who Survived on the Titanic? Predictive Classification with Parametric and Non-parametric Models

December 24, 2012
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Who Survived on the Titanic? Predictive Classification with Parametric and Non-parametric Models

I recently read a really interesting blog post about trying to predict who survived on the Titanic with standard GLM models and two forms of non-parametric classification tree (CART) methodology. The post was featured on R-bloggers, and I think it's worth a closer look. The basic idea was to figure out which of these three

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High-Powered Statistical Computing On the iPad

December 17, 2012
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High-Powered Statistical Computing On the iPad

It's winter break… and as any academic knows, breaks are “a good time to get work done.” For the Christmas break, many of us have to travel home to see family members. One of the great privileges of being an academic is that you don't necessarily need to be in your office to get research

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