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.
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.
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. Continue Reading
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. Continue Reading
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 We Learn
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.