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Charts and graphs are great, because they can let you see a pattern that you might not see in a spreadsheet, but they only work when you use the actual data. Fox News isn't doing themselves any favors by putting up this chart. It shows the recently announced drop in unemployment rate to 8.6 percent as a non-change.
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The argument behind this graph in The Wall Street Journal is that the middle class has most of the money and ties into a larger argument about who should be taxed what. There is after all a spike in the middle. Is that really the case though? Sound off in the comments.
(Cheat sheet: Jonathan Chait explains what's going on and Kevin Drum improves the graph to show more truth, although his graph can be improved, too. Grab the data here [Excel spreadsheet] from the IRS, and give it a go.)
[Wall Street Journal via @joandimicco]
President Barack Obama delivered his State of the Union address yesterday, and this year it was "enhanced" by charts and graphs. Basically, as Obama spoke, graphics that you could equate to Powerpoint slides showed up on the side. What'd you think of the enhancement? Did it add or detract from the message? Were the graphics used honestly and effectively?
One thing's for sure: there's something wrong with that bubble chart. Uh oh.
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I was going to post this graphic from Good when it came out, but decided not to. I made the same mistake when I first started out. It was another case of wrongly sized bubbles. But they fixed the problem, so now we can see what a big difference it makes. Continue Reading
Shady research from Harvard scientist Marc Hauser is confirmed:
On Friday, Michael D. Smith, dean of the Harvard faculty of arts and sciences, issued a letter to the faculty confirming the inquiry and saying the eight instances of scientific misconduct involved problems of “data acquisition, data analysis, data retention, and the reporting of research methodologies and results.” No further details were given.
This is why we don't just accept any old data and why we care about the methodology behind the numbers. Stuff like this always reminds me of an exam question that asked us to investigate the data from an article in a prominent scientific journal. The analysis was all wrong.
Sometimes data is wrong out of ignorance. Other times it's wrong because people make stuff up. I can understand the former, but why you would ever do the latter is beyond me.
[via]
Update: More details on what happened from research assistants' point of view on the Chronicle. [thx, Winawer]
BP senior vice president Kent Wells explains in this new video what his group is doing towards repairing the leak. He presents the bar graph above to show the improvement in their efforts. It's increasing, so they must be improving. Nifty. The problem is that it's cumulative, and the rate at which they're collecting isn't improving.
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No clue where this is from, but something seems sort of off, no? I guess we should take the title literally. By the numbers... only.
I'm going to give the benefit of the doubt though, and assume this was just an honest mistake. Here's my guess about what happened. A deadline was coming up quick, and a graphics editor put this together to get a feel for what the final design would look like. He then saved it as a different file, and then went to work. Except when it came time to send the file to the printers, the editor sent the wrong file. Actually, now that I think about it, I'm surprised this doesn't happen more often.
[via @EagerEyes]
What? I don't see anything wrong with it.
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This graphic on the history and future of information has been making the rounds. Several people sent it to me a while back, but it didn't seem quite right, so I didn't post it; however, this post from PZ Meyers compelled me to take another look. Meyers says:
Some days, I think other people must be aliens. Or I must be. For instance, there's a lot of noise right now about this article analyzing the future of information and media that, if you read the comments, you will discover that people are praising to an astonishing degree. I looked at it and saw this graph [above graphic]. And my bullshit detector went insane. It's supposed to be saying something about where people are and will be getting their information, but there's no information about where this information came from, and it's meaningless!
Yikes. Take out the boxing gloves. Looks like we've got another clash between the technical and the design-ish and mainstream crowds. The comments from both sides are also pretty interesting with one group saying how visually appealing and informative the graphic is with the other group criticizing the graphic for failing in every way.
Clearly the graphic is not based on any real data or metric. It goes off history and probably a lot of Wikipedia entries, and then shapes and sizes go off feeling. So as an analytical graph, it doesn't work. But what about as an opinion in graph form? Does it work then? What do you think? Is this graphic a crime against all that is good in visualization or does it work for what it was trying to do?
[Thanks, Patrick]
If there's anything good that has come out of America's financial crisis, it's the interesting and high-quality infographics. This isn't one of them. Below is an ill-conceived bubble chart from BillShrink that "shows" average U.S. consumer spending. Notice anything wrong with it?
Bar versus bubble debate aside, there is a ton of room for improvement as well as huge need for some fact-checking and common sense. For a blog on a site for personal finance, the graphic is, well, not something to be proud of. FlowingData readers know that I like to stay away from heavy-handed critique on what works and what doesn't (I leave that to you guys), but this BillShrink graphic is just so clearly confusing that it's worth pointing out what doesn't work so we can learn from others' mistakes. Can you find the flaws?
[Thanks, Jess]
I know next to nothing about the economy, stocks, and investments, but I do know a little bit about charts and graphs. The above area circles were prepared by someone at JP Morgan. I don't know, you might have heard of 'em. The circles are based on data from Bloomberg and meant to show the change in market value from 2007 to 2009. The problem here is that the creator sized circles by diameter instead of area, so the difference looks ginormous. I mean, the value change is significant but not that big.
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This video shows statistics centered around atheism, claiming that atheism is correlated with a healthy society. I don't want to turn this into a religious debate, but I really don't like these types of videos, slide shows, etc. It's not the ideas that bother me, but because some people think it's a great idea to rattle off a bunch of numbers to "prove" a point. Nevermind the biases, invalid studies, poor analysis, cruddy data, and "results" taken out of context.
What do you think? Do you buy this stuff?
Peter Donnelly talks about the misuse of statistics in his TED talk a couple of years back. The first 2/3 of the talk is an introduction to probability and its role in genetics, which admittedly, didn't get much of my interest. The last third, however, gets a lot more interesting.
Donnelly talks about a British woman who was wrongly convicted largely in part because of a misuse of statistics. A so-called expert cited how improbable it would be for two children to die of sudden infant death syndrome, but it turns out that "expert" was making incorrect assumptions about the data. This doesn't surprise me since it happens all the time.
People misuse statistics every day (intentionally and unintentionally), and oftentimes it doesn't hurt much (which doesn't make it any better), but in this case improper use directly affected someone's life in a very big way. One of the most common assumptions I see is that every observation is independent, which often is not the case. As a simple example, if it's raining today, does that change the probability that it will rain tomorrow? What it didn't rain today?
In other words, the next time you're thinking of making up or tweaking data, don't; and the next time you need to analyze some data but aren't sure how, ask for some help. Statisticians are nice and oh so awesome.
Here's Donnelly's talk:
On Last.fm, someone took snapshots of some Linkin Park songs, compared them, and concluded that all Linkin Park songs look are the same. I guess at a glance, the songs might appear the same because of the dark chunk towards middle left, but it kind of stops there. Sure, there's some loud to soft and soft to loud alternation, but who likes songs who are loud (or soft) throughout?
The beginning of the post:
Each image above shows the audio level in (roughly) the first 90 seconds of a Linkin Park song. The tempo has been adjusted for a few tracks for better visual alignment.
Wait a minute. The tempo was adjusted for better visual alignment? If you're adjusting the tempo, then really, all songs can be made to look the same. On top of that, we don't know the x-axis or y-axis units. Finally, there's a lot more to a song other than dynamics -- such as key, tempo, rhythm, and lyrics.
I saw this map of the average snow levels in Buffalo. I think I just glanced at it and that was about it. When you first look at the map, what do you make of the colors? When I see green for snow levels, I think no snow. Am I crazy? What do you think?
So the image was kind of in my head all this summer while I was in NYC. When I told people that I was going back to Buffalo after my internship, they always gave this look that said, "Ha, have fun during the winter," and then they would actually say it and then go into how they measure the snow level by comparing it against a giant pole.
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After parsing Weather Underground pages to grab temperature data, it's time to look at the data. Can't download all that data and not do anything with it!
First off, in my initial pass of my parsing script, I accidentally cut the month range short, so I didn't get any data for December from 1980 to 2005. It should be noted that these plots don't show this missing data. Um, there's no axes or labels either. Sorry, I got a little lazy, but that's not the point now anyways.
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It's easy to see how Statistics got this bad wrap because it's so easy to lie with data, charts, and graphs. Sometimes it's on purpose -- someone might try to present "good" results that actually suck. Sometimes it's accidental -- someone might have misread or didn't read the documentation that came with the data. In the case of Swivel's most recently featured graph, it was the latter. A case of mistaken identity so to speak.
The data about doping tests in sports came from here. Now the graph on Swivel would have you believe that the data represent the number of doping cases found in each sports; however, according to the USADA report, the data is actually the number of tests the association conducted inside and outside competition during the first quarter of this year. The report contains no data on the USADA's findings.
What can we learn from this? It's great to visualize data, but you have to be careful. Read the documentation. Find out what the data is about, because without context, the visualization or any findings are practically useless. Statistics isn't to lie. In fact, it's the exact opposite. Statistics came about and exists today to reveal the truth.
FlowingData explores how designers, statisticians, and computer scientists are using data to understand ourselves better — mainly through data visualization. More...
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