Video of the week: playing with stats

From TED talks comes this excellent presentation delivered by Hans Rosling, a professor of global health at Karolinska Institute (the institution that decides upon the Nobel prize in medicine). The focus of his talk is on disproving the myths about the developing world, which is, according to Rosling, actually quickly closing the gap with the rich countries and is moving forward the same direction (following the same pattern) of the Western world. They are now where the West was some 40-50 years ago. Basically he claims, and I agree, that we shouldn't generalize aid policies towards one area, since within this area there are huge within and across-country inequalities. The same policy on curing diseases cannot be applied towards the top income groups in South Africa and the low income groups in Nigeria (as he mentions in the video; or in this great slideshow: Africa is not a country!). There's many more good points being made, so I recommend the video: 



He has a few more videos on TED (this was the earliest one, from 2006), but the web page he mentions, Gapminder, is truly amusing and features a bunch of interesting statistics and graphics. 

For example, here is a lecture on the changes brought by the industrial revolution - the global development since 1800. Video is here. From an average income per person of less than $3000 and a life expectancy of around 40 years, and with all countries clustered pretty densely on the bottom left end (life expectancy on the upper axis, income p/c on the lower axis), over the course of 200 years you can witness a rapid change in development patterns of some world areas, while others - even though they were falling behind on this development scale - still saw an increase of income and health standards. The slide show is here - notice how the big leap in living standards for the poor countries starts somewhere around the 1950s, but their income growth didn't follow until the last decade or so. 

Of course, observing and playing with stats is not everything. Econometricians will often say 'correlation does not imply causality', and they're right. However interesting correlations found in a variety of useful data can give pretty good hints on what to start your research on. Sometimes you hopefully get something out of it. 

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