Just a few days ago many of the most incredible minds in science and technology urged governments to avoid using artificial intelligence to create autonomous killing machines. One thing that always happens when such a warning is put into place is you see the inevitable Terminator picture: The reality is that robots that
Moneyball is a book and a movie about Billy Bean. It makes statisticians look awesome and I loved the movie. I loved it so much I’m putting the movie trailer right here: The basic idea behind Moneyball was that the Oakland Athletics were able to build a very successful baseball team on a tight budget by valuing
The second capstone session of the Johns Hopkins Data Science Specialization concluded recently. This time, we had 1,040 learners sign up to participate in the session, which again featured a project developed in collaboration with the amazingly innovative folks at SwiftKey. We've identified the learners listed below as the top performers in this capstone session.
There has been a lot of recent interest from scientific journals and from other folks in creating checklists for data science and data analysis. The idea is that the checklist will help prevent results that won't reproduce or replicate from the literature. One analogy that I'm frequently hearing is the analogy with checklists for surgeons that can
Editor's note: We are trying something a little new here and doing an interview with Google Hangouts on Air. The interview will be live at 11:30am EST. I have some questions lined up for Chris, but if you have others you'd like to ask, you can tweet them @simplystats and I'll see if I can
Editor’s note: This post was inspired by a really awesome career planning guide that Ben Langmead wrote up for his postdocs which you should go check out right now. You can also find the slightly adapted Leek group career planning guide here. The most common reason that people go into science is altruistic. They loved
Editor's note: I have been unsuccessfully attempting to finish a book I started 3 years ago about how and why everyone should get pumped about reading and understanding scientific papers. I've adapted part of one of the chapters into this blogpost. It is pretty raw but hopefully gets the idea across. An episode of The Daily Show with
There are a lot of analogies between startups and academic science labs. One thing that is definitely very different is the relative value of ideas in the startup world and in the academic world. For example, Paul Graham has said: Actually, startup ideas are not million dollar ideas, and here's an experiment you can try
Joe Pickrell has an interesting new paper out about Mendelian randomization. He discusses some of the interesting issues that come up with these studies and performs a mini-review of previously published studies using the technique. The basic idea behind Mendelian Randomization is the following. In a simple, randomly mating population Mendel's laws tell us that at any
Editor's note: I thought it would be fun to do some bibliometrics on a Friday. This is super hacky and the CAR/Y stat should not be taken seriously. I downloaded data on the 400 most cited papers between 2000-2010 in some statistical journals from Web of Science. Here is a boxplot of the average number
