First up, a post that relates to an earlier Friday link. In that one, the students in a computer science class successfully employed an unstable game theory strategy on a test. In this one, the professor in a Behavioral Ecology course at UCLA (Peter Nonacs) made the exam into a game, which he calls “flipping the test”. Nonacs concludes that it had this effect on his students: “they were changing their goal in the Education Game from “Get a higher grade than my classmates” to “Get to the best answer.”” So, Jeremy: you called the earlier link “the greatest example of applied game theory EVER”. Do you think this tops that?
Next up: Another week, another call for more academics to blog (or, at least, write for a popular audience). First, this piece from the Chronicle of Higher Education is a commentary by an assistant professor of cell biology at Yale, on what he learned from writing an op-ed piece. Second, this blog post from Titus Brown, in which he advises grad students to have an online presence.
Which brings me to this new article in PLoS Biology, entitled “An Introduction to Social Media for Scientists“, by Holly Bik and Miriam Goldstein. I haven’t had a chance to read the whole article yet, but it lays out different online tools/resources/sites, and gives advice on how to use them effectively. (Jeremy adds: But our readers already know why you should read blogs, how to blog, and why you should use Twitter! And conversely, why you shouldn’t!)
Statistician Larry Wasserman of the Normal Deviate blog has a thought-provoking post on how current trends towards “Big Data” and computationally-intensive analyses are causing scientists to turn away from collaboration with statisticians and towards collaboration with computer scientists. Here’s the key quote:
Here is a short parable: A scientist comes to a statistician with a question. The statistician responds by learning the scientific background behind the question. Eventually, after much thinking and investigation, the statistician produces a thoughtful answer. The answer is not just an answer but an answer with a standard error. And the standard error is often much larger than the scientist would like.
The scientist goes to a computer scientist. A few days later the computer scientist comes back with spectacular graphs and fast software.
Who would you go to?
I am exaggerating of course. But there is some truth to this. We statisticians train our students to be slow and methodical and to question every assumption. These are good things but there is something to be said for speed and flashiness.
I’m curious what folks think of this. Do you think the trends Larry identifies are happening in ecology?
Mathbabe on being an alpha female in academia, finance, and other intellectual fields. Related to things Meg and I have talked about. Curious what folks, particularly female readers, think of this. Do you think of yourself, or women you admire in science, as “alpha females”? Do you consciously set out to be one, or know anyone who does?
Fun with full text searches: use of subjective language in history papers is increasing. Would be interesting to do this sort of thing with scientific articles, to track both changes in content (e.g., rise and fall of bandwagons) and changes in style.
Well-known author A. J. Jacobs has a schtick: he sets himself some ridiculous self-improvement task (read the entire Encyclopedia Brittanica; live according to a literal interpretation of the Bible for a year; etc.) and then writes about the results. His latest mini-stunt: he took a bunch of MOOCs. An entertaining snapshot of the current state of play in massive open online education.
The number of statistics majors at Harvard is growing exponentially (up 10x since 2006), apparently thanks largely to an outstanding prof taking over the intro stats course. Why, it’s almost as if many students don’t start college with their interests and skills set in stone, and will take to quantitative subjects if they’re taught well! My undergrad college had much the same experience a couple of decades or so ago. Math back then was a very small department, with few majors. But the department hired a number of truly outstanding teachers, and today math at Williams is quite a popular major, and one of the most popular double majors on campus.
The journal Nature is beefing up standards for reporting of statistics and methods, and for sharing of raw data. The goal is to promote reproducibility.
Unsurprising-but-still-depressing department: Republicans in the US House of Representatives have floated the ideas of requiring every NSF grant application to include a statement on how the research would “benefit the American people”, and of evaluating NSF grants according to their likely contribution to “the national interest”. I’m not too worried about the first idea, since in practice it’s pretty easy to address that sort of thing (maybe not in the way that House Republicans would like, of course). Whether or not to worry about the second idea depends on how likely it is to be adopted, I think. But the larger worry is that both ideas are just symptoms of a much broader agenda.
And finally, a novel benefit of data sharing: it not only allows grad students the occasional opportunity to absolutely school famous Harvard professors, it lands said grad students appearances on the Colbert Report. 😉
“So Jeremy…Do you think this tops that?”
Given how eager you were to claim this link for the Friday linkfest, do I dare disagree…
Each example is great in its own way. This one is in a game theory class. In the previous post, I did say that the only thing that would improve on the previous example would be if it occurred in a game theory class. On the other hand, here the prof obliged the students to play the game, though of course it was up to the students how to play it. Whereas in the previous example the students organized so as to game the exam all by themselves, which I think is more awesome. So let`s call it a tie. 😉
“Do you think the trends Larry identifies are happening in ecology?”
Yes. I was nearly gobbled up, arriving in an ecology department with a computer science degree. I had to turn down offers to collaborate as a first-year, because — you know — I had *classes* to take.
“Curious what folks, particularly female readers, think of this. Do you think of yourself, or women you admire in science, as “alpha females”?”
Of course; generally speaking, for women to succeed in any highly competitive environment dominated by men, they have to be aggressive, loud-spoken, and impervious to intentional and unintentional attacks on their abilities and selves. This is not a secret. Those that aren’t leave to find a more pleasant place to spend their lives. But there are gradations. I can tell you that ecology is a *much* more pleasant place to be a woman than computer science, and so I find I can be “nicer” here and still succeed (although aggressiveness, loudness, and thick skin still needed to some extent).
I have no idea what Wasserman is talking about – his metaphor sounds completely unlike what I experience. I feel like I’m surrounded by methodical people who are obsessed with errors and few people who do programming, let alone flashy stuff. Also I don’t understand his contrast of ‘flashy graphs’ to data large error bars. What kind of graphs don’t have error bars?
In fact, I wish there were more CS people around working on data visualization. Trying to get a simple graph together is one of my most frustrating experiences. Yes, yes, R. But R is actually really bad at giving you a simple graph and letting you modify it easily. I hate to say it but Excel is actually what I use for a simple line graph when I don’t have access to/money for SigmaPlot. R is much better for complex graphics and I’ve seen some really beautiful figures from it. I just don’t have that much time to invest in learning every time I want to plot something.
Alpha female? Meh. Let’s say I’m chill and social for a scientist, and anxious and argumentative for a regular human. Being female only makes that contrast weirder. That said, I’ve consciously worked on my sociability and presentation to tone myself down/relax.
Might help to have a look at the comments on Larry’s post to get more of a sense of what he’s talking about. Plenty of his commenters seem to “get” where he’s coming from. I suspect your comment may reflect the fact that “big data” is just starting to make its way into ecology. The contrast Larry’s raising isn’t something that comes up if you’re just dealing with the usual sorts of ecological datasets. It’s the kind of thing that comes up when you have truly massive datasets–say, many thousands or even millions of observations or something, on scads of variables.
Re: graphing in R, I’m no expert, but have you tried ggplot2? Everyone who’s serious about graphing n R seems to swear by it…
I’m still not sure that it’s an apt metaphor or parable – that scientists will ignore statistics in favour of flashy graphs once we have more data? It seems to assume that the only reason we’re interested in statistics to begin with is to compensate for our data. One of the things that makes us scientists is using statistics to test hypotheses. Big data might complicate things but I don’t see it shifting the philosophy of science that much.
Oh and I haven’t tried ggplot2 and will. Thanks.
The link regarding the increase in stats majors at Harvard is broken.
Thanks Amy, fixed.