Science is about both ideas and data gathering.

Stan asked for my thoughts on this article: Science is becoming a cult of hi-tech instruments – Philip Ball – Aeon.

The tools of science are so specialised that we accept them as a kind of occult machinery for producing knowledge. We figure that they must know how it all works. Likewise, histories of science focus on ideas rather than methods — for the most part, readers just want to know what the discoveries were. Even so, most historians these days recognise that the relationship between scientists and their instruments is an essential part of the story. It isn’t simply that the science is dependent on the devices; the devices actually determine what is known. You explore the things that you have the means to explore, planning your questions accordingly.

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.

The gist of this article appears to be an assertion that scientists shouldn’t let data gathering crowd out the formulation of ideas, which are crucial for hypotheses and theories.  My reaction is, well yeah, but it’s not like ideas are something that can be produced on demand or on a schedule.  And data gathering is what separates science from other ways of learning things, and I think its credibility is largely due to it.

On the importance of ideas front, I think this is one of the reasons I find scientists who dismiss philosophical speculation to be so short sighted.  The truth is, philosophical reasoning produces many hypotheses that might someday be testable.  Indeed, the line between scientific speculation and philosophical speculation is hazy and largely artificial.  Often the only difference is that one takes place in a science department and the other in the philosophy department.  Yes, there are ideas that clearly have a very low probability of ever being testable, and others that can foreseeably be tested in a few years, but there’s a lot in between in the grey zone.

But again, ideas don’t always arrive on demand.  And what do we do in a dry spell?  We can speculate harder, but our speculation will be richer if we have more data to work with.  That’s why there are many scientists whose primary mission is data gathering.  They themselves might not ever produce a theory from that data, but by making it available, they increase the probability that someone somewhere will find a pattern and produce a new theory.

I’m reminded of the history of astronomy.  For centuries, the Ptolemaic understanding of the universe was the reigning model, with Earth at the center of the universe and everything else orbiting it.  There were many issues with that model, but it seemed to explain observations better than other ones at the time.  Faced with that situation, astronomers could have simply accepted the Ptolemaic model and done something else.  But instead, they continued observing and recording ever more detailed observations, for centuries.

When we think of people like Copernicus, we also should think about the innumerable astronomers who left them centuries of recorded data at observatories around the world with which to formulate their theories.  And when it comes to scientific instruments, we should also bear in mind what the telescope did for astronomy.  Galileo saw more in a few brief years than astronomers had for millennia before him.

All of which is to say, that data gathering is crucial.  And it shouldn’t be regarded as any second class activity.  Data gathering is often hard unglamourous work, with astronomers having to work late at night and in strange locations, or biologists having to crawl through dirt, mud, and who knows what else to get specimens, geologists having to go to dangerous and harsh locations to get samples, or anthropologists having to spend years living among natives.

Ideas are crucial to successful science, but so is data gathering.  Pretending that we can have one without the other, or that we should value one over the other, strikes me as a false dichotomy.  We need both, and when ideas are in short supply, or we have lots of ideas that can’t be tested (<cough>theoretical physics</cough>), then data gathering for a while strikes me as an imminently sensible thing to do.

h/t Stan Hummel

5 thoughts on “Science is about both ideas and data gathering.

  1. Bill Bryson’s wonderful book “A Short History of Nearly Everything” discusses in layman’s terms the entire state of modern science and is an excellent read for anyone interested in science. Time and again Bryson recounts the interplay between ideas, discoveries, data collection and last, but not least, the genius and persistence of the men and women who made it happen.


  2. What came to mind was Thomas Kuhn’s Structure of Scientific Revolutions. In his discussion of paradigms, he talks about ‘normal science’ where data collecting and incremental change occurs. At a certain point, the data and the theory can no longer be reconciled. When that happens, the paradigm collapses and space opens up for new ideas.

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  3. It’s interesting that some of the biggest discoveries have happened because someone used an instrument to gather data that no one thought was interesting. Like the astronomers who decided to look at the sky in the infrared. Everyone thought it was a waste of money but they ended up finding the clouds of gas that form stars that no one new existed

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