This is an eye-opening visualization of “cognitive surplus” from www.informationisbeautiful.net:
Thus, in the amount of time U.S. adults spend watching TV in one year, roughly 2000 Wikipedia-style projects could be created.
I once heard someone suggest a wikipedia as a unit of measure of collective human cognitive output. So every year, U.S. adults spend 2000 wikipedias watching TV. How can we more efficiently harness this cognitive surplus?
An interesting article in the New York Times (with a cool interactive graphic) digs down into the data of “on-time performance” for the three major NYC-area railways–LIRR, Metro North, and NJT.
http://www.nytimes.com/2010/07/27/nyregion/27ontime.html
The official figure is that 96% of trains arrive on time. This may be hard for the average commuter to believe, but it is technically true. What accounts for this discrepancy between perception and performance?
As the data shows, trains were far more likely to run behind schedule during peak times (rush hours) than during non-peak times. This makes intuitive sense–more people on the trains, more trains running simultaneously, and so more potential problems and delays. But this also means that a delay during rush hour will affect more people, and thus have a greater impact on the perception of the train’s timeliness than, say, a late-night lateness.
Furthermore, there are only a couple of “rush” hours, so the total number of rush-hour trains is less than the total number of non-rush-hour trains. The latter are more likely to run on time, and so their aggregate impact on overall performance dominates the calculation.
To simplify, if 5 out of 10 rush hour trains are late (50%), and 5 out of 50 non-rush hour trains are late (10%), then the overall lateness ratio is 10 out of 60, or about 17%. But a lot more people are riding the rush hour trains, and to them, it seems like the trains are late half the time. This is a example of the Inspection Paradox.
There are also some interesting psychological principles at work here: for example, people will generally think more about the one time they are late than, say, the 10 times the train ran on-time. It will be even more significant if that lateness ends up costing you something–like a good impression on a client or a boss.
This is an awesome demonstration of world-wide trends in life expectancy by Hans Rosling. The graph uses regions of changing size, location, and color to show the dynamic history of the data.
http://www.youtube.com/watch?v=QHCt6PmSs0k
I happened upon this here, where there is much more appreciation of Rosling, the “zen master of presenting data”, and his work.
David Blackwell was a highly-regarded statistician and mathematician who taught at UC Berkeley for 30 years. Apparently he was the kind of mathematician who could become interested in a new topic, learn about it, and then quickly produce profound results. Then he’d move on. Blackwell died on July 8th: his obituary in the NYT can be found here.
Among other things, Blackwell was a strong proponent of the Bayesian approach to statistical inference, and he produced results in Game Theory regarding bluffing and dueling.
A friend claimed that this video about Mariano Rivera contained the best info-graphic ever made, and it’s hard to argue.
The whole thing is fascinating, but at about the 1:40 mark the 1300 hundred or so pitches Rivera threw in 2009 are tracked from the mound and plotted in the strike zone. Not only is it a supremely cool visualization of statistical analysis, but it absolutely helps explain why Rivera is so successful (and, indeed, one of the greatest pitchers in baseball history).