Essentially, all models are wrong, but some are useful.

— George E. P. Box, Empirical Model-Building and Response Surfaces, 1987

A favorite quote among statisticians.

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We're statisticians. We don't program.

I was talking to a small group of statisticians a few months ago, and someone said that to me when I told them how I go about mucking around with data. It still annoys me just thinking about it. It wasn't that he didn't know how to program — because that's perfectly understandable — but he said it in a way as if programming and statistics were so separate that there was no possible way the two could go together.

Wrong.

Let's set things straight before this silly idea spreads further. Programming and statistics belong together, and you don't have to be a coding genius for it to work.

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Far better an approximate answer to the right question, which is often vague, than an exact answer to the wrong question, which can always be made precise.

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There is no way to think up an original and extraordinary design—it can only come as a result of pursuing a given task. In the same way running down a list of words is different from making a narrative.

This applies to visualization too. When you don't have a question to answer or a simple wonderment about something, you end up staring at a bunch of numbers with no clue what to do with them. Want to test this out? Go to data.gov and make something useful.

[via @Coudal]

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We’re inventors - we’re creators. And that’s the most important thing about what we do. And I think we should welcome failure every once in a while.

Last year at Malofiej, one of the major awards ceremonies for infographics in journalism, The New York Times took home 'Best in Show' for their work on box office receipts from 1986 to 2008. I'm sure most of you saw it. It was non-traditional. It was an adaptation of Lee Byron's streamgraph, which he had previously applied to last.fm music listening habits - a smoothed stacked area chart at the core.

What followed was a lot of back chatter among the infographic community. Many didn't like the interactive at all, despite winning an award voted on by peers. Some called it one of the worst graphics NYT had ever published, that it was too complicated for readers, and that it was too hard to read.

This surprised me.

It's something I've come to expect from academics and the stat crowd but not from graphic departments that report the news. I had the impression that they were more open-minded, but I guess not all of them are.

This, from Hannah Fairfield, a New York Times graphics editor, is no doubt a response to the haters at this year's Malofiej.

I obviously strongly agree. Although Hannah sort of implies that the streamgraph was a failure. I'd argue that it was a success. Hundreds of thousands of people, millions maybe, engaged with the box office data and there's no obscene misrepresentations. Were the patterns too complex to understand for some people? Yeah, probably, but how else is the general public supposed to learn? They'll get there eventually.

Your turn. Does the box office streamgraph work?

[via VisualJournalism]

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I'm thankful for the opportunity I had to work at Google. I learned more than I thought I would. I'll miss the free food. I'll miss the occasional massage. I'll miss the authors, politicians, and celebrities that come to speak or perform. I'll miss early chances to play with cool toys before they're released to the public. Most of all, I'll miss working with the incredibly smart and talented people I got to know there. But I won't miss a design philosophy that lives or dies strictly by the sword of data.

I feel like I've been hearing a lot of this type of stuff recently. Just yesterday, while watching NBA playoff commentary, someone stated that future Detroit Lions quarterback, Matthew Stafford, had something like a 1 in 4 chance of success in the NFL. Charles Barkeley replied that sometimes you gotta forget about stats and just go, or something to that effect. Oddly enough, I agree.

[via TechCrunch]

UPDATE: Kevin Fox, (formerly) senior user experience design lead at Google, responded to Bowman's post: "I don't think Google had to be a bad fit for you, but that you were put in to the wrong role." [Thanks, David]

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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?

Varian then goes on to say:

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'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.

Wait a minute. Is this a pitch for FlowingData? I think so :). Check out the full article for more (or listen to the podcast). It's an interesting read.

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We're quite addicted to data pr0n here at Flickr. We've got graphs for pretty much everything, and add graphs all of the time.

— John Allspaw, Operations Engineering Manager at Flickr

[via O'Reilly Radar | Thanks, Jodi]

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Most people make the mistake of thinking design is what it looks like… People think it's this veneer -- that the designers are handed this box and told, 'Make it look good!' That's not what we think design is. It's not just what it looks like and feels like. Design is how it works.

I post this not because I like Apple products, but because it's true (and because I like Apple products). I'm no designer, but as a statistician, I have tremendous respect for those who are. Have a nice weekend all.

[via swissmiss]

Martin Wattenberg, one of the creators of Many Eyes, in reply to "Why is a numbers guy like you so interested in large textual data sets?"

The entire literary canon may be smaller than what comes out of particle accelerators or models of the human brain, but the meaning coded into words can't be measured in bytes. It's deeply compressed. Twelve words from Voltaire can hold a lifetime of experience.

Martin Wattenberg = smart guy.

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