# 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

## Measuring Bias in Published Work

July 31, 2013
By
$Measuring Bias in Published Work$

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

## 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

## 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

## How to make a scientific result disappear

February 27, 2013
By
$How to make a scientific result disappear$

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

## 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

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

February 4, 2013
By
$Proposed techniques for communicating the amount of information contained in a statistical result$

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

## 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

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

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

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

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

December 24, 2012
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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