Every couple of years, the International Association of Statistics Education hosts a Roundtable discussion, wherein researchers, statisticians, and curriculum developers are gather from around the world to share ideas. The 2012 Roundtable, held in Cebu City, the Philippines, focused on the role of Technology in Statistics Education, and so, after a very long time editing (for me and Jennifer Kaplan) and re-writing (for our authors), we are now ready to present the Roundtable Special Edition.
[](http://lifehacker.com/)
The website Lifehacker recently had an article about some common statistical misconceptions. I thought they did a great job explaining things like the base-rate fallacy and Simpson’s Paradox for a lay audience. I also really liked the extrapolation cartoon they picked. [Read the whole article here.]
Two years ago, my department created a new two-course, doctoral-level sequence primarily aimed at our quantitative methods students. This sequence, aside from our students, also attracts students from other departments (primarily in the social sciences) that plan to pursue more advanced methodological coursework (e.g., Hierarchical Linear Modeling).
One of the primary characteristics that differentiates this new sequence of courses from the other doctoral sequence of methodology courses that we teach is that it is “more rigorous”.
What do we fear more? Losing data privacy to our government, or to corporate entities? On the one hand, we (still) have oversight over our government. On the other hand, the government is (still) more powerful than most corporate entities, and so perhaps better situated to frighten.
In these times of Snowden and the NSA, the L.A. Times ran an interesting story about just what tracking various internet companies perform. And it’s alarming.
Technology Innovations in Statistics Education has published a paper by Noleine Fitzallen that I think many readers of this blog will find interesting. She examines a group of young students to see the ways that they use Tinkerplots to analyze data. Here’s the abstract and link:
Characterising Students' Interaction with TinkerPlots
Exploration of the way in which students interacted with the software package, TinkerPlots Dynamic Data Exploration, to answer questions about a data set using different forms of graphical representations, revealed that the students used three dominant strategies – Snatch and Grab, Proceed and Falter, and Explore and Complete.
It just seems to me that this is what data science was meant to do: give us fun toys. This particular “toy”, called Every Noise at Once, lets you explore the musical universe. Ours may be the last blog to comment on it–I think I stumbled upon this too late. But it provides a great example for our students about the power of data analysis.
The data come from the company EchoNest, and the visualization (although that’s a weak word for this—its visual and aural, maybe “visauralization”?
You can win $1000 for turning your Ph.D. thesis into an interpretive dance. More importantly, you will also receive a call-out from _Science _and get to perform your dance at TEDX in Belgium. This contest is not only open to more recent Ph.D.s, but anyone who got a Ph.D. (in the sciences) and also to students working on a Ph.D.
Gonzolabs has tips and examples over on their website. So put on your dancing shoes, grad your Ph.
