Blog Archives

Kobe, data says stop blaming your teammates

December 12, 2014
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Kobe, data says stop blaming your teammates

This year, Kobe leads the league in missed shots (by a lot), has an abysmal FG% of 39 and his team plays better when he is on the bench. Yet he blames his teammates for the Lakers' 6-16 record. Below is a plot showing that 2014 is not the first time the Lakers are mediocre during Kobe's

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Genéticamente, no hay tal cosa como la raza puertorriqueña

December 8, 2014
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Genéticamente, no hay tal cosa como la raza puertorriqueña

Editor's note: Last week the Latin American media picked up a blog post with the eye-catching title "The perfect human is Puerto Rican". More attention appears to have been given to the title than the post itself. The coverage and comments on social media have demonstrated the need for scientific education on the topic of

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Thinking Like a Statistician: Social Media and the ‘Spiral of Silence’

December 2, 2014
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Thinking Like a Statistician: Social Media and the ‘Spiral of Silence’

A few months ago the Pew Research Internet Project published a paper on social media and the ‘spiral of silence’. Their main finding is that people are less likely to discuss a controversial topic on social media than in person. Unlike others, I  did not find this result surprising, perhaps because I think like a statistician.

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HarvardX Biomedical Data Science Open Online Training Curriculum launches on January 19

November 25, 2014
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We recently received funding from the NIH BD2K initiative to develop MOOCs for biomedical data science. Our first offering will be version 2 of my Data Analysis for Genomics course which will launch on January 19. In this version, the course will be turned into an 8 course series and you can get a certificate in each one

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Data Science Students Predict the Midterm Election Results

November 12, 2014
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Data Science Students Predict the Midterm Election Results

As explained in an earlier post, one of the homework assignments of my CS109 class was to predict the results of the midterm election. We created a competition in which 49 students entered. The most interesting challenge was to provide intervals for the republican - democrat difference in each of the 35 senate races. Anybody

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538 election forecasts made simple

November 4, 2014
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Nate Silver does a great job of explaining his forecast model to laypeople. However, as a statistician I've always wanted to know more details. After preparing a "predict the midterm elections" homework for my data science class I have a better idea of what is going on. Here is my best attempt at explaining the ideas of

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Why I support statisticians and their resistance to hype

October 28, 2014
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Despite Statistics being the most mature data related discipline, statisticians have not fared well in terms of being selected for funding or leadership positions in the new initiatives brought about by the increasing interest in data. Just to give one example (Jeff and Terry Speed give many more) the White House Big Data Partners Workshop  had

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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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