[Hpn] Statistics are everywhere - but that's no reason to believe them

coh coh@sfo.com
Thu, 05 Jul 2001 12:43:06 -0700


Christian Science Monitor

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Statistics are everywhere - but that's no reason to believe them

By Steve Weinberg 

Deep down, educated people know they put too much faith in the statistics
they read: 300,000 individuals die each year in the United States from
illness related to obesity? "Wow!" we say, failing to think through the
special interest of groups, such as the American Obesity Association,
involved in disseminating that number; failing to analyze why a newspaper
article from The Associated Press citing that number might need to be
doubted; failing to question how such a specific number can be determined
without qualification.

Joel Best, a sociology and criminal- justice professor at the University of
Delaware, reminds us that we need to treat statistics skeptically. Best is
not the first to sound the warning, but his book, "Damned Lies and
Statistics," is a clearly written primer for the statistically impaired. It
is as important to discussions of public policy as any book circulating

Best is persuasive early in the book that citizens do not have to feel
helpless about numbers. Most individuals handle small numbers well. As he
says in a homely but effective example, "Everyone understands that it makes
a real difference whether there'll be three people or 30 coming by tomorrow
night for dinner. A difference (30 is ten times greater than three) that
seems obvious with smaller, more familiar numbers gets blurred when we deal
with bigger numbers."

As an example of a bigger number, Best uses the purported count of homeless
in the United States. Is it 300,000 or 3 million? Nobody really knows. Yet
many citizens are willing to accept uncritically the smaller estimate or the
estimate 10 times larger, believing that either way it's a large number
indicating a possibly intractable problem.

Such uncritical acceptance baffles and angers Best: "If society is going to
feed the homeless, having an accurate count is just as important as it is
for an individual planning to host three - or 30 - dinner guests."

By way of example, he introduces us to a caring soul working at a shelter
for runaways. That person decides to collect statistics for one month on the
shelter's clients. But do the individuals passing through that month
represent all runaways? Of course not. Many runaways never seek shelter at
such places. So, do the clients that month at least represent runaways who
turn to shelters? Probably not. If it's winter, the clients might be
different from those who appear in the summer, to mention just one of many

To think wisely about such numbers, Best suggest thinking through these

€ Who created this statistic?

€ Why was this statistic created?

€ How was this statistic created?

The answers to those questions usually show that statistics are not created
in a vacuum, that each creator of a statistic has a
political-social-economic agenda, no matter how well-intentioned.

Best explains the four basic sources of flawed statistics (bad guesses,
deceptive definitions, confusing questions, biased samples). Then he
examines mutant statistics and discusses the illogic of statistical
comparisons that attempt to equate differing time periods, places, groups,
or social problems.

In his introduction, he recalls a statistic from a doctoral dissertation
which claimed that every year since 1950, the number of American children
gunned down had doubled. Huh? Simple multiplication would have shown that
such doubling each year was illogical. By 1987, for instance, the number of
gunned-down children (137 billion) would have surpassed all of the world's
population throughout history.

Best peppers the text with such compelling examples. Anti-stalking groups
somehow arrived at an estimate of 200,000 persons being regularly harassed
throughout the country. Within months, that estimate had hardened into fact,
with no meaningful attribution, no explanation of methodology. Journalists
became part of the problem, as they often are in such instances, by failing
to scrutinize the claims.

Is it possible to actually prove anything with statistics? For a brief
passage, Best sounds something like Bill Clinton, claiming it depends on
what "prove" means. Some questions are answerable with access to the best
statistics possible. Other questions are not. That sounds so obvious. But
the obvious has yet to sink in.

€ Steve Weinberg is a journalist in Columbia, Mo.
Copyright 2001 The Christian Science Monitor

**In accordance with Title 17 U.S.C. section 107, this material is
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interest in receiving this type of information for non-profit research and
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