Understanding uncertainty

+math magazine ^ | September 2007 | by Marianne Freiberger

[em2vn]

This opens up all areas of life to statistical treatment, carefully balanced with human judgement. "Take MRSA for example. Each night there are about 100,000 people being cared for in English hospitals, and about 18 of those will be diagnosed with MRSA infection. That figure is pretty constant, so at a national level you can make stable predictions of how many people will contract the disease. But to assess an individual's risk is another matter. You could say it's 18 in 100,000, but that's just for an average person. In reality everyone is unique. The risk will vary according to where a patient lives, what kind of ward they're in, how old they are, and many other factors. Maybe they are an MSRA carrier and it's very likely that they'll get the disease."

[em2vn]

A good read.

[Some Fat Guy in L.A.]

This guy has the best job in the world! (says a stats nerd)

That said - you can do statistical analysis on most anything, as long as the underlying assumptions and data are accurate, and as long as your sample is representative of the population. One bad assumption, incorrect data set or a biased sample invalidates the analysis and makes the whole thing worse than useless (bad information being worse than no information - as we have seen with the gorbal warming mess).