Monthly Archives: April 2013

Reproducibility and reciprocity

One element about the entire discussion about reproducible research that I haven't seen talked about very much is the potential for the lack of reciprocity. I think even if scientists were not concerned about the possibility of getting scooped by … Continue reading

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Sunday data/statistics link roundup (4/28/2013)

What it feels like to be bad at math. My personal experience like this culminated in some difficulties with Green's functions back in my early days at USU. I think almost everybody who does enough math eventually runs into a … Continue reading

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Mindlessly normalizing genomics data is bad - but ignoring unwanted variability can be worse

Yesterday, and bleeding over into today,¬†quantile normalization (QN) was being discussed on Twitter. This is the tweet that started the whole thing off. The conversation went a bunch of different directions and then this happened: well, this happens all over … Continue reading

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Interview at Yale Center for Environmental Law & Policy

Interview with Roger Peng from YCELP on Vimeo. A few weeks ago I sat down with Angel Hsu of the Yale Center for Environmental Law and Policy to talk about some of their work on air pollution indicators. (Note: I … Continue reading

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Nevins-Potti, Reinhart-Rogoff

There's an interesting parallel between the Nevins-Potti debacle (a true debacle, in my mind) and the recent Reinhart-Rogoff kerfuffle. Both were exposed via some essentially small detail that had nothing to do with the real problem. In the case of … Continue reading

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Podcast #7: Reinhart, Rogoff, Reproducibility

Jeff and I talk about the recent Reinhart-Rogoff reproducibility kerfuffle and how it turns out that data analysis is really hard no matter how big the dataset.

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I wish economists made better plots

I'm seeing lots of traffic on a big-time economics article by that failed to reproduce and here are my quick thoughts. You can read a pretty good summary here by Mike Konczal. Quick background: Carmen Reinhart and Kenneth Rogoff wrote … Continue reading

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Data science only poses a threat to (bio)statistics if we don't adapt

We have previously mentioned on this blog how statistics needs better marketing. Recently, Karl B. has suggested that "Data science is statistics" and Larry W. has wondered if "Data science is the end of statistics?" I think there are a … Continue reading

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Sunday data/statistics link roundup (4/14/2013)

The most influential data scientists on Twitter, featuring Amy Heineike, Hilary Mason, and a few other familiar names to readers of this blog. In other news, I love reading list of the "Top K _____" as much as the next … Continue reading

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Great scientist - statistics = lots of failed experiments

E.O. Wilson is a famous evolutionary biologist. He is currently an emeritus professor at Harvard and just this last week dropped this little gem in the Wall Street Journal. In the piece, he suggests that knowing mathematics is not important … Continue reading

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