the use and abuse of science

http://economix.blogs.nytimes.com/2008/12/05/why-does-us-health-care-cost-so-much-part-iii-an-aging-population-isnt-the-reason

"Why Does U.S. Health Care Cost So Much?"
by Uwe E. Reinhardt
December 5, 2008
The New York Times


This article is very nice, for some reasons. The one I'm most interested in is its use of these two graphs:



Basically, it argues that, by means of the first graph (combined with another which I omit), it can be made to appear that health care costs for the eldery are going to explode and add a ton of cost to the U.S. health care system. However, if we draw the graph differently, it becomes clear that this is not the case, and that the aging of the U.S. (as is stated) is a very gradual process.

The funny thing is that, if one thinks about it, even an 8% shift in the balance of elderly / non-elderly could have a tremendous impact on health care costs. That is, neither of these graphs really tells us anything. As it happens, the author backs up his claims with some studies that (purport) to show that increasing health care costs are not driven by aging. But the general point remains: it is very easy to lie with science, if one's audience is not attentive.

So, consider another graph provided by the author to prove his point:

This graph makes it appear that there is no correlation between the age-ratio of a population, and health care costs. But one might want to ask: what about controlling for other variables? Maybe if we control for, say, total population size, or economic output per capita, or who knows what, all of a sudden that cloud of dots will snap into a sharp line. So again, while it is a powerful image, there needs to be a lot more science and statistics done to back it up.

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In other words, to return to some comments made earlier in this blog about the power of science: the lesson to be drawn is that it's easy enough to use the trappings of science - graphs, charts, statistics - to give the appearance of truth and validity to what are, in reality, questionable conclusions. But where do we go from here? Do we claim that science (as everything?) must rely on a degree of mutual trust - trust that people are working honestly and to the best of their abilities? Or do we say that people need to be more aware in their own dealings, and need to spend more time on analyzing the various facts they are presented with? (Do we all need to have taken courses on statistics?)