Stats in the Wild

I was just checking out the bernoulli trial blog and Stan has posted a great quote from google’s chief economist about how sexy I am. Well, it’s about how sexy statisticians are. The job anyway. (My friend who is doing a Ph. D. in English also sent me this highly relevant article from the New York Times: “For Today’s Graduate, Just One Word: Statistics”.)

“I keep saying the sexy job in the next ten years will be statisticians. People think I’m joking, but who would’ve guessed that computer engineers would’ve been the sexy job of the 1990s? The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades, not only at the professional level but even at the educational level for elementary school kids, for high school kids, for college kids. Because now we really do have essentially free and ubiquitous data. So the complimentary scarce factor is the ability to understand that data and extract value from it.

I think statisticians are part of it, but it’s just a part. You also want to be able to visualize the data, communicate the data, and utilize it effectively. But I do think those skills—of being able to access, understand, and communicate the insights you get from data analysis—are going to be extremely important. Managers need to be able to access and understand the data themselves.

You always have this problem of being surrounded by “yes men” and people who want to predigest everything for you. In the old organization, you had to have this whole army of people digesting information to be able to feed it to the decision maker at the top. But that’s not the way it works anymore: the information can be available across the ranks, to everyone in the organization. And what you need to ensure is that people have access to the data they need to make their day-to-day decisions. And this can be done much more easily than it could be done in the past. And it really empowers the knowledge workers to work more effectively.”

– Hal Varian, Google’s chief economist in The McKinsey Quarterly, January 2009

Cheers.

Here is a blog post (from Andrew Gelman’s blog) about an article (also by Andrew Gelman) about the misuse of statistics, if you will, in the wild.

From his blog: “The article begins as follows:

In the past few years, Satoshi Kanazawa, a reader in management and research methodology at the London School of Economics, published a series of papers in the Journal of Theoretical Biology with titles such as “Big and Tall Parents Have More Sons” (2005), “Violent Men Have More Sons” (2006), “Engineers Have More Sons, Nurses Have More Daughters” (2005), and “Beautiful Parents Have More Daughters” (2007). More recently, he has publicized some of these claims in an article, “10 Politically Incorrect Truths About Human Nature,” for Psychology Today and in a book written with Alan S. Miller, Why Beautiful People Have More Daughters.
However, the statistical analysis underlying Kanazawa’s claims has been shown to have basic flaws, with some of his analyses making the error of controlling for an intermediate outcome in estimating a causal effect, and another analysis being subject to multiple-comparisons problems. These are technical errors (about which more later) that produce misleading results. In short, Kanazawa’s findings are not statistically significant, and the patterns he analyzed could well have occurred by chance. Had the lack of statistical significance been noticed in the review process, these articles would almost certainly not have been published in the journal. The fact of their appearance (and their prominence in the media and a popular book) leads to an interesting statistical question: How should we think about research findings that are intriguing but not statistically significant? . . .”

Note: Have you ever heard anyone say something like, “You know ‘they’ say beautiful parents have more daughters.” Ever wondered who the “they” is they were talking about? In this case, it’s Kanazawa. And he’s wrong. So, sometimes even “they” are wrong. Pretty scary cause most people trust the “they”.

Cheers.

Here is some good advice from Andrew Gelman about writing research articles.

Cheers.

The Harvard stats blog has an interesting post about the distribution of swine flu cases by day.

Cheers.

I was just alerted by an avid reader that SPSS has been purchased by IBM. Details in this article: “IBM’s purchase of SPSS ends era”.

Cheers.

One of my professors does a lot of work with copulas, and today I was reading a poster presentation that he did where he cites this article, Recipe for Disaster: The Formula That Killed Wall Street. The author, Felix Salmon, talks about how the Gaussian copula formula brought down wall street. Judging solely by his title, it seems as if we should blame the formula, and only the formula, for the collapse of the financial markets, rather than the bankers who egregiously misused the formula. But writers love making black and white issues out of the grayest of circumstances.

Cheers.

Here is a paper by some professors ar Carnegie Mellon regarding the privacy (lack of privacy) of using social security nubmers as identifiers. Here is an article about the paper.

Disclaimer: SITW in no way endorese the stealing of SSNs. In fact, we don’t endorse stealing anything.

Cheers.

Here are two good articles I received as links in my email recently.

Here is a recent article from Slate.com about golf stats and predicting winners. Good stuff.

And here is another Slate.com article about people lying about who they voted for in the last presidential election. They delve a little bit into sampling methodology.

It’s nice to see people usings stats…..in the wild.

Cheers.

I know it’s been a while.

Here is an article about a poll gone wrong thanks to poor sampling design.

Cheers.