October 2006

Predicting Elections

Jacob Eisenstein at MIT has developed an smart election predictor for the US Senate Elections using a Kalman Filter. The filter helps to decide how much extra weight to attach to more recent polls. Check it out here; he also has some details on the method here.

More thoughts on publication bias and p-values

Amy Perfors

In a previous post about the Gerber & Malhotra paper about publication bias in political science, I rather optimistically opined that the findings -- that there were more significant results than would be predicted by chance, and that many of those were suspiciously close to 0.05 -- were probably not deeply worrisome, at least for those fields in which experimenters could vary the number of subjects run based on the significance level achieved thus far.


Here’s an interesting piece that should help you keep your New Semester resolutions by understanding procrastination better. Sendhil Mullainathan recently used research by Dan Ariely and Klaus Wertenbroch as motivation for his undergraduate psychology and economics class. Though it’s not exactly statistics, it seems the insights could be useful for grad students and their advisors.