A spam e-mail making the rounds in the military community serves as a reminder that facts can be flexible when they are launched anonymously into the vast void of cyberspace.
The e-mail, entitled, “Some very interesting statistics: Military losses, 1980 through 2006,” states that more U.S. service members died on active duty during the eight years of the Clinton administration, when there were no major U.S. military conflicts, than in the first six years of the George W. Bush administration, during which the military was fighting two large-scale wars in Afghanistan and Iraq.
The e-mail offers year-by-year U.S. military death totals from all causes — operations, illness, accidents, suicides, etc. — from 1980 through 2006.
The data supposedly were taken from a periodically updated Congressional Research Service report on the subject, which in turn is based on statistics compiled by the Pentagon’s Defense Manpower and Data Center.
There’s just one problem: The figures listed in the email are wrong. They vary markedly from the figures published in the cited CRS source document.
...
The claims of this particular e-mail are easily disproved. But the online proliferation of such anonymous documents highlights a serious concern for researchers and scholars about how to separate fact from fiction within the vast quantities of raw material online — and being consumed by users who often have no easy way to gauge the reliability of the information they see.
The Web site of The Sheridan Libraries, the main research facility at Johns Hopkins University, includes a lengthy “how to” guide for evaluating Internet information that underscores the difficulty.
“When you use a research or academic library, the books, journals and other resources have already been evaluated by scholars, publishers and librarians,” the Web site states. “Every resource you find has been evaluated in one way or another before you ever see it.”
Online, however, “none of this applies — there are no filters,” the library Web site states.
FreePint Family
Quilting big data threads
The fallacy of information overload
Information overload: fact, fantasy or filter failure? 
