Science
19 November 1999:
Vol. 286. no. 5444, pp. 1460  1464
DOI: 10.1126/science.286.5444.1460
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NEWS FOCUS
STATISTICS:
Bayes Offers a 'New' Way to Make Sense of Numbers
David Malakoff
A 236yearold approach to statistics is making a comeback, as its ability to factor in hunches
as well as hard data finds applications from pharmaceuticals to fisheries
After 15 years, environmental researcher Kenneth Reckhow can still feel the sting of rejection.
As a young scientist appearing before an Environmental Protection Agency review panel,
Reckhow was eager to discuss his idea for using an unorthodox statistical approach in a
waterquality study. But before he could say a word, an influential member of the panel
unleashed a rhetorical attack that stopped him cold. "As far as he was concerned, I was a
Bayesian, and Bayesian statistics were worthless," recalls Reckhow, now at Duke University
in Durham, North Carolina. "The idea was dead before I even got to speak."
Reckhow is no longer an academic outcast. And the statistical approach he favors, named
after an 18th century Presbyterian minister, Thomas Bayes, now receives a much warmer
reception from the scientific establishment. Indeed, Bayesian statistics, which allows
researchers to use everything from hunches to hard data to compute the probability that a
hypothesis is correct, is experiencing a renaissance in fields of science ranging from
astrophysics to genomics and in realworld applications such as testing new drugs and setting
catch limits for fish. The longdead minister is also weighing in on lawsuits and public policy
decisions (see p.
1462
), and is even making an appearance in consumer products. It is his
ghost, for instance, that animates the perky paperclip that pops up on the screens of
computers running Microsoft Office software, making Bayesian guesses about what advice
the user might need. "We're in the midst of a Bayesian boom," says statistician John Geweke
of the University of Iowa, Iowa City.
Advances in computers and the limitations of traditional statistical methods are part of the
reason for the new popularity of this old approach. But researchers say the Bayesian
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View Full Documentapproach is also appealing because it allows them to factor expertise and prior knowledge
into their computationssomething that traditional methods frown upon. In addition, advocates
say it produces answers that are easier to understand and forces users to be explicit about
biases obscured by reigning "frequentist" approaches.
To be sure, Bayesian proponents say the approach is no panaceaand the technique has
detractors. Some researchers fear that because Bayesian analysis can take into account prior
opinion, it could spawn less objective evaluations of experimental results. "The problem is that
prior beliefs can be just plain wrong" or difficult to quantify properly, says statistician Lloyd
Fisher of the University of Washington, Seattle. Physicians enthusiastic about a particular
treatment, for instance, could subtly sway trial results in their favor. Even some advocates
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 Spring '10
 albert
 Statistics, Bayesian probability, Bayesian inference, Bayesian statistics

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