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Researchers are using Bayesian statistics to tackle problems of formidable complexity, from narrowing down the age of the universe to hunting for MH370.
Statistics may not sound like the most heroic of pursuits. But if not for statisticians, a Long Island fisherman might have died in the Atlantic Ocean after falling off his boat early one morning last summer.
The man owes his life to a once obscure field known as Bayesian statistics, a set of mathematical rules for using new data to continuously update beliefs or existing knowledge.
The method was invented in the 18th century by an English Presbyterian minister named Thomas Bayes — by some accounts to calculate the probability of God’s existence. In this century, Bayesian statistics has grown vastly more useful because of the kind of advanced computing power that didn’t exist even 20 years ago.
It is proving especially useful in approaching complex problems, including searches like the one the Coast Guard used in 2013 to find the missing fisherman, John Aldridge (though not, so far, in the hunt for Malaysia Airlines Flight 370).
Now Bayesian statistics are rippling through everything from physics to cancer research, ecology to psychology. Enthusiasts say they are allowing scientists to solve problems that would have been considered impossible just 20 years ago. And lately, they have been thrust into an intense debate over the reliability of research results.
When people think of statistics, they may imagine lists of numbers — batting averages or life-insurance tables. But the current debate is about how scientists turn data into knowledge, evidence and predictions. Concern has been growing in recent years that some fields are not doing a very good job at this sort of inference. In 2012, a team at the biotech company Amgen announced that they’d analysed 53 cancer studies and found it could not replicate 47 of them.
Similar follow-up analyses have cast doubt on so many findings in fields such as neuroscience and social science that researchers talk about a “replication crisis”.
Some statisticians and scientists are optimistic that Bayesian methods can improve the reliability of research by allowing scientists to crosscheck work done with the more traditional or “classical” approach, known as frequentist statistics. The two methods approach the same problems from different angles.
The essence of the frequentist technique is to apply probability to data. If you suspect your friend has a weighted coin, for example, and you observe that it came up heads nine times out of 10, a frequentist would calculate the probability of getting such a result with an unweighted coin. The answer (about 1 per cent) is not a direct measure of the probability that the coin is weighted; it’s a measure of how improbable the nine-in-10 result is — a piece of information that can be useful in investigating your suspicion.
By contrast, Bayesian calculations go straight for the probability of the hypothesis, factoring in not just the data from the coin-toss experiment but any other relevant information, including whether you’ve previously seen your friend use a weighted coin.
Scientists who have learned Bayesian statistics often marvel that it propels them through a different kind of scientific reasoning than they’d experienced using classical methods.
“Statistics sounds like this dry, technical subject, but it draws on deep philosophical debates about the nature of reality,” said Princeton University astrophysicist Edwin Turner, who has witnessed a widespread conversion to Bayesian thinking in his field over the last 15 years.
In other fields, researchers are using Bayesian statistics to tackle problems of formidable complexity. New York University astrophysicist David Hogg credits Bayesian statistics with narrowing down the age of the universe. As recently as the late 1990s, astronomers could say only that it was eight billion to 15 billion years old; now, factoring in supernova explosions, the distribution of galaxies and patterns seen in radiation left over from the Big Bang, they have concluded with some confidence that the number is 13.8 billion years.
One downside of Bayesian statistics is that it requires prior information — and often scientists need to start with a guess or estimate.
Others say that in confronting the so-called replication crisis, the best cure for misleading findings is not Bayesian statistics, but good frequentist ones. It was frequentist statistics that allowed people to uncover all the problems with irreproducible research in the first place, said Deborah Mayo, a philosopher of science at Virginia Tech. The technique was developed to distinguish real effects from chance, and to prevent scientists from fooling themselves.
The Coast Guard has been using Bayesian analysis since the 1970s. The approach lends itself well to problems like searches, which involve a single incident and many different kinds of relevant data, said Lawrence Stone, a statistician for Metron, a scientific consulting firm in Reston, Virginia, that works with the Coast Guard.
At first, all the Coast Guard knew about the fisherman was that he fell off his boat sometime between 9 pm on July 24 and 6 the next morning. The information went into a programme called SAROPS, for Search and Rescue Optimal Planning System. Over the next few hours, searchers added new information — on prevailing currents, places the search helicopters had already flown and some clues found by the boat’s captain.
The system couldn’t deduce exactly where Aldridge was drifting, but with more information, it continued to narrow down the most promising places to search.
Just before turning back to refuel, a searcher in a helicopter spotted a man clinging to two buoys he had tied together. He had been in the water for 12 hours; he was hypothermic and sunburned but alive.
Even in the jaded 21st century, it was considered something of a miracle.