Kaiser Fung talks about the suck of overlaying plots to show a relationship.

When the designer places two series on the same chart, he or she is implicitly saying: there is an interesting relationship between these two data sets.

But this is not always the case. Two data sets may have little to do with each other. This is especially true if each data set shows high variability over time as in here.

This seems to happen a lot when people take the data-to-ink ratio too literally or they’re trying too hard to be clever within a given space. Overlays work on occasion, but I can’t think of any that did off the top of my head. Most of the time it’s better to split up the layers into multiple charts.

In a study by TheLadders (of n equals 30), recruiters looked at resumes and make some judgments. During evaluations, eye tracking software was employed, and they found that the recruiters spent about six seconds on a resume looking for six main things: name, current company and title, previous company and title, previous position start and end dates, current position start and end dates, and education. After that, it was a crapshoot.

Beyond these six data points, recruiters did little more than scan for keywords to match the open position, which amounted to a very cursory “pattern matching” activity. Because decisions were based mostly on the six pieces of data listed above, an individual resume’s detail and explanatory copy became filler and had little to no impact on the initial decision making. In fact, the study’s eye tracking technology shows that recruiters spent about 6 seconds on their initial “fit/no fit” decision.

If I ever have to submit a resume, I’m just going to put those six things as bullets and then the rest will all be keywords in small, light print. It’ll be like job search SEO.

Update: I’ve been told that TheLadder’s reputation might be less than savory, and a quick search shows some in agreement, so it might be wise to sidestep the service. Instead, go with my awesome six-bullet advice and you’re gold.

[via @alexlundry]

The Washington Post asked three “young entrepreneurs” how their company uses infographics. They responded with similar sentiments. The first one said:

Infographics can be great as part of presentations, newsletters or other research content. It keeps people’s interest by lending a storytelling and visual element to what can be sterile research.

The second said:

Infographics are outstanding for bringing life to content that would otherwise be dry, uninteresting or unshareable.

And the last one, who to be fair, seems to know more than the first two, said:

At the end of the day, the main use for infographics is to create content that can potentially go viral and drive traffic, links and exposure to a Web site and the brand.

If I were new to these infographic things, my main takeaway here would be that they’re used to make boring material interesting. Shouldn’t it be the other way around though? Information graphics are interesting because their foundations of data and um, information are worth looking at in the first place. Don’t fall into the trap of trying to make something “visually compelling” without anything to compel with.

In usual xkcd fashion, Randall Munroe plots the depths of lakes and oceans, including “mysterious door which James Cameron built his sub to reach and open.”

By Reddit user depo_, this map showing metal bands per capita around the world is making the rounds. Clear dominance in Sweden and Finland.

The New York Times, in collaboration with the New York University Movement Lab, explains music conducting in this beautifully produced video. It’s part interview with Alan Gilbert, music director of the New York Philharmonic, and part rendering of motion capture data, which represents Gilbert’s conducting.

To capture the data, the Movement Lab installed high-speed motion capture cameras, and Gilbert put on one of those funny-looking suits with the sensor balls on them. He conducted, and they recorded his body and his hands.

Fantasia will probably come to mind as you watch, specifically towards the end when only conducting trails and sensor spots are left to dance on the screen.

George E.P. Box, a statistician known for his body of work in time series analysis and Bayesian inference (and his quotes), recounts how he became a statistician while trying to solve actual problems. He was a 19-year-old college student studying chemistry. Instead of finishing, he joined the army, fed up with what the British government was doing to stop Hitler.

Before I could actually do any of that I was moved to a highly secret experimental station in the south of England. At the time they were bombing London every night and our job was to help to find out what to do if, one night, they used poisonous gas.

Some of England’s best scientists were there. There were a lot of experiments with small animals, I was a lab assistant making biochemical determinations, my boss was a professor of physiology dressed up as a colonel, and I was dressed up as a staff sergeant.

The results I was getting were very variable and I told my colonel that what we really needed was a statistician.

He said “we can’t get one, what do you know about it?” I said “Nothing, I once tried to read a book about it by someone called R. A. Fisher but I didn’t understand it”. He said “You’ve read the book so you better do it”, so I said, “Yes sir”.

Box eventually worked with Fischer, studied under E. S. Pearson in college after his discharge from the army, and started the Statistical Techniques Research Group at Princeton on the insistence of one John Tukey.

After reading another article about the flood of data that we’re drowning and struggling to stay afloat in, I wondered, “If everyone is drowning in data, does that mean statisticians are the life preservers?” Some agreed, but others went a slightly different route. Some said plumbers, and others said lifeguards. Someone said they’re the annoying kid doing cannonballs.

The metaphor seems to change depending on where you’re sitting and what body of water you’re in, so just for kicks and giggles, let’s see how far we can stretch this metaphor. If data is the tsunami and people are drowning, what does that make statisticians, data scientists, and information designers? Plus points for ridiculousness.