Blog Archives

Thinking like a statistician: don’t judge a society by its internet comments

October 20, 2014
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Thinking like a statistician: don’t judge a society by its internet comments

In a previous post I explained how thinking like a statistician can help you avoid  feeling sad after using Facebook. The basic point was that missing not at random (MNAR) data on your friends' profiles (showing only the best parts of their life) can result in the biased view that your life is boring and uninspiring in

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Bayes Rule in an animated gif

October 17, 2014
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Bayes Rule in an animated gif

Say Pr(A)=5% is the prevalence of a disease (% of red dots on top fig). Each individual is given a test with accuracy Pr(B|A)=Pr(no B| no A) = 90% .  The O in the middle turns into an X when the test fails. The rate of Xs is 1-Pr(B|A). We want to know the probability

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I declare the Bayesian vs. Frequentist debate over for data scientists

October 13, 2014
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I declare the Bayesian vs. Frequentist debate over for data scientists

In a recent New York Times article the "Frequentists versus Bayesians" debate was brought up once again. I agree with Roger: NYT wants to create a battle b/w Bayesians and Frequentists but it's all crap. Statisticians develop techniques. http://t.co/736gbqZGuq — Roger D. Peng (@rdpeng) September 30, 2014 Because the real story (or non-story) is way too

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Applied Statisticians: people want to learn what we do. Let’s teach them.

September 15, 2014
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In this recent opinion piece, Hadley Wickham explains how data science goes beyond Statistics and that data science is not promoted in academia. He defines data science as follows: I think there are three main steps in a data science … Continue reading →

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Academic statisticians: there is no shame in developing statistical solutions that solve just one problem

July 25, 2014
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I think that the main distinction between academic statisticians and those calling themselves data scientists is that the latter are very much willing to invest most of their time and energy into solving specific problems by analyzing specific data sets. … Continue reading →

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The Big in Big Data relates to importance not size

May 28, 2014
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The Big in Big Data relates to importance not size

In the past couple of years several non-statisticians have asked me "what is Big Data exactly?" or "How big is Big Data?". My answer has been "I think Big Data is much more about "data" than "big". I explain below. … Continue reading →

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Confession: I sometimes enjoy reading the fake journal/conference spam

April 30, 2014
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I've spent a considerable amount of time setting up filters to avoid getting spam from fake journals and conferences. Unfortunately, they are exceptionally good at thwarting my defenses. This does not annoy me as much as I pretend because, secretly, … Continue reading →

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Correlation does not imply causation (parental involvement edition)

April 17, 2014
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The New York Times recently published an article on education titled "Parental Involvement Is Overrated". Most research in this area supports the opposite view, but the authors claim that "evidence from our research suggests otherwise".  Before you stop helping your children … Continue reading →

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Writing good software can have more impact than publishing in high impact journals for genomic statisticians

April 7, 2014
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Every once in a while we see computational papers published in science journals with high impact factors.  Genomics related methods appear quite often in these journals. Several of my junior colleagues express frustration that all their papers get rejected from these journals. … Continue reading →

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Data Analysis for Genomics edX Course

March 31, 2014
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Mike Love (@mikelove) and I have been working hard the past couple of months preparing a free online edX course on data analysis for genomics. Our target audience are the postdocs, graduate students and research scientists that are tasked with … Continue reading →

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