Posts filed under Careers (31)

September 2, 2013

Evidence-based interviewing?

Two links,

Deciding who to interview: Aline Lerner looked at resumes of 300 candidates interviewed at a Silicon Valley company to see what predicted getting the job. The biggest factor wasn’t grades or degree or experience, it was typos  — and this was among people who got an interview.

Did it work? An interview with a Google exec by the New York Times

We looked at tens of thousands of interviews, and everyone who had done the interviews and what they scored the candidate, and how that person ultimately performed in their job. We found zero relationship. It’s a complete random mess, except for one guy who was highly predictive because he only interviewed people for a very specialized area, where he happened to be the world’s leading expert.

August 2, 2013

The methods behind the statistics do matter

From the US 6th Circuit Court of Appeals (PDF), in a lawsuit alleging false advertising by a US law school, based on a low-quality survey of graduates

For example, the Employment Report for 2010 states that the “average starting salary for all graduates” was $54,796. On its face, the phrase “all graduates” means just that: all Cooley graduates—not just the ones who responded to the survey—made, on average, $54,796. One could assume that, because there were 934 graduates, the average starting salary for all 934 graduates was $54,796. The title of the document containing this statement is “Employment Report and Salary Survey.”  Therefore, it cannot be that the average starting salary of all 2010 graduates was $54,796, because the document, entitled “Employment Report and Salary Survey” (emphasis added) was not based on the responses of all of the Cooley graduates in 2010; rather, the document states that the number of 2010 graduates was 934, but the number of graduates with employment status known was 780. So, the “[a]verage starting salary for all graduates” would instead mean the average starting salary of graduates who responded to the survey and chose to include their salary information—not the average salary of all Cooley graduates in any given year.

We agree with the district court that this statistic is “objectively untrue,” but that the graduates’ reliance upon it was “also unreasonable,”  which dooms their fraudulent misrepresentation claim.

It’s not just statisticians who think you need to pay attention to where the numbers come from.

(via)

May 28, 2013

Analytics is beating statistics

icrunchdata, which is a data-related jobs site, has introduced what it is calling the Big Data Jobs Index

crunchy

 

If you believe the numbers, it looks as though analytics is way ahead in the synonym game, followed by data science, but at least statistics is still ahead of business intelligence. And at least this is a bar chart, though not an index in any usual sense of the term.

The company describes Big Data as having ‘fueled one of the most hyper-growth niches of employment in a century’, but since their projection is for the sector to grow to nearly 1% of the US job market by 2015, we clearly need to be careful of the definition of fast growth

May 17, 2013

How do you get a career in statistics?

This is a common question from our students. Unfortunately our perspective does not always lend itself easily to life outside of research and academia, as what I look for in a curriculum vitae and in a job interview is usually with respect to hiring someone who will become an academic staff member. However, fellow statistician, and the Young Statisticians representative for the New Zealand Statistical Association executive committee, Kylie Maxwell has posted her own experience as part of the International Year of Statistics.

 

May 16, 2013

Future of applied statistics

Rafa Irizarry at Simply Statistics has a longish piece on the future of applied statistics:

Despite having expertise only in music, and a thesis that required a CD player to hear the data, fitted models and residuals , I was hired by the Department of Biostatistics at Johns Hopkins School of Public Health. Later I realized what was probably obvious to the School’s leadership: that regardless of the subject matter of my thesis, my time series expertise could be applied to several public health applications. The public health and biomedical challenges surrounding me were simply too hard to resist and my new department knew this. It was inevitable that I would quickly turn into an applied Biostatistician.

It makes a nice change from the people worrying that computer science will beat us up and steal our lunch.

April 27, 2013

New science journalists

Three journalists who have just finished internships in science journalism with NPR news in Washington, DC. Might be useful to follow in the future:

 

April 25, 2013

Infographic of the week

Every so often, someone comes up with a creative way to make pie charts less informative.  This week’s innovation comes to you from Wired magazine.

explodedpie

Note that it’s structured like a bar chart, except that all the `bars’ are the same height, and the wedges are turned at different angles, to make the widths harder to estimate.  The numbers are presented as if their heights mean something, but actually not.

There are also some subtleties to the design.  For example, at first glance you might think the left-to-right order of the wedges reflects the time period each one corresponds to, so that the fact they aren’t largest to smallest means something. Sadly, no.

(via @acfrazee and @kwbroman)

April 8, 2013

Briefly

  • Interesting post on how extreme income inequality is. The distribution is compared to a specific probability model, a ‘power law’, with the distribution of earthquake sizes given as another example. Unfortunately, although the ‘long tail’ point is valid, the ‘power law’ explanation is more dubious.   Earthquake sizes and wealth are two of the large number of empirical examples studied by Aaron Clauset, Cosma Shalizi, and Mark Newman, who find the power law completely fails to fit the distribution of wealth, and is not all that persuasive for earthquake sizes. As Cosma writes

If you use sensible, heavy-tailed alternative distributions, like the log-normal or the Weibull (stretched exponential), you will find that it is often very, very hard to rule them out. In the two dozen data sets we looked at, all chosen because people had claimed they followed power laws, the log-normal’s fit was almost always competitive with the power law, usually insignificantly better and sometimes substantially better. (To repeat a joke: Gauss is not mocked.)

 

April 1, 2013

Briefly

Despite the date, this is not in any way an April Fools post

  • “Data is not killing creativity, it’s just changing how we tell stories”, from Techcrunch
  • Turning free-form text into journalism: Jacob Harris writes about an investigation into food recalls (nested HTML tables are not an open data format either)
  • Green labels look healthier than red labels, from the Washington Post. When I see this sort of research I imagine the marketing experts thinking “how cute, they figured that one out after only four years”
  • Frances Woolley debunks the recent stories about how Facebook likes reveal your sexual orientation (with comments from me).  It’s amazing how little you get from the quoted 88% accuracy, even if you pretend the input data are meaningful.  There are some measures of accuracy that you shouldn’t be allowed to use in press releases.
March 27, 2013

Does data visualisation matter?

“I wish there were more examples where data viz actually mattered. The case studies for us to lean on are sparser than they should be.”

Amanda Cox, NY Times chartmaker, interviewed at Harvard Business Review.  Includes a graph showing how the same unemployment report might be viewed by partisans of opposing parties.