Simply generating pointless data and spurious correlations is a real risk, science writer Philip Ball suggests
The United States and European Union have recently announced two immense projects, costing hundreds of millions of dollars each, to map out the human brain, using the latest imaging techniques to trace every last one of the billions of neural connections. Some neuroscientists are drooling at the thought of all that data. ‘Think about it,’ one told Nature in July 2013. ‘The human brain produces in 30 seconds as much data as the Hubble Space Telescope has produced in its lifetime.’
If, however, we wanted to know how cities function, creating a map of every last brick and kerb would be an odd way to go about it. Quite how these brain projects will turn all their data into understanding remains a mystery. One researcher in the EU-funded project, simply called the Human Brain Project and based in Switzerland, inadvertently revealed the paucity of theory within this information glut: ‘It is a chicken and egg situation. Once we know how the brain works, we’ll know how to look at the data.’ Of course, the Human Brain Project isn’t quite that clueless, but this hardly mitigates the enormity of this flippant statement. Science has never worked by shooting first and asking questions later, and it never will.
The faddish notion that science will soon be a matter of mining Big Data for correlations, driven in part by the belief that data is worth collecting simply because you have the instruments to do so, has been rightly dismissed as ludicrous. It fails on technical grounds alone: data sets of any complexity will always contain spurious correlations between one variable and another. But it also fails to acknowledge that science is driven by ideas, not numbers or measurements — and ideas only arise by people thinking about causative mechanisms and using them to frame good questions. The instruments should then reflect the hypotheses, collecting precisely the data that will test them.
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