Rebecca Goldin, George Mason University
Tuesday, October 28, 2008
Abstract: News increasingly depends on a careful dissection of numbers. Statistics are everywhere, from how many people lack health insurance to how to improve math education. Yet for being so prevalent, statistics are badly understood by the general public.
Mark Twain popularized the quote that "There are three kinds of lies: lies, damn lies, and statistics." While this quote suggests the scary idea that statistics can be manipulated to say anything, I will argue that statistics can tell us lots of useful things when used appropriately, and that the more the media does this for us, the more educated we can be as news consumers, and the better we will be at truly evaluating risk for ourselves and others.
In this talk, I'll illustrate how the press can misuse and even abuse statistics using examples of news coverage. Since news sources are the main avenue by which the public understands many public health issues, these misguided representations of science can actually shape public policy, legislation, and individual choices. We will see why it is so important that media writers understand basic concepts from statistics, epidemiology and even toxicology. I will also show how powerful the work can be when the press goes beyond politics and morality to point out what science says, what it doesn't, and what it can't.
Biography: Rebecca Goldin is a professor of mathematics at George Mason University. She received her undergraduate degree from Harvard, and her PhD from MIT. She taught at University of Maryland as a National Science Foundation postdoctoral fellow before joining George Mason in 2001. She currently serves as the Director of Research for Statistical Assessment Service (STATS), a nonprofit media education and watchdog group affiliated with George Mason. When she's not thinking about statistics in the media, she's pursuing her research interests in group actions on manifolds and symplectic geometry. Last year, Goldin won the Ruth I. Michler Memorial Prize for mathematics.
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