July 17, 2008, 10:31 am
The End of the Scientific Method
By Lisa Agustin
According to Chris Anderson at Wired, the scientific method is no longer relevant, thanks to the enormous amounts of data now at our disposal. The traditional (and sometimes imperfect) approach of testing hypotheses via modeling made more sense when scientists were trying to understand the underlying mechanisms that connect a handful of results. This is no longer the case:
This is a world where massive amounts of data and applied mathematics replace every other tool that might be brought to bear. Out with every theory of human behavior, from linguistics to sociology. Forget taxonomy, ontology, and psychology. Who knows why people do what they do? The point is they do it, and we can track and measure it with unprecedented fidelity. With enough data, the numbers speak for themselves.
Anderson gives a couple of examples to prove his point, including how new species of bacteria were “discovered” using high-speed sequencers and supercomputers (“a statistical blip”). The idea that data is the starting point, and relationships and rationale can be established later is not a new idea for data viz practitioners, but thinking about this approach in the context of dismissing other methodologies? I’m not so sure.
Looks like I’m not the only one that thinks Anderson’s approach is a little drastic: Cosmic Variance noted:
“Sometimes it will be hard, or impossible, to discover simple models explaining huge collections of messy data taken from noisy, nonlinear phenomena. But it doesn’t mean we shouldn’t try. Hypotheses aren’t simply useful tools in some potentially outmoded vision of science; they are the whole point. Theory is understanding, and understanding our world is what science is all about.” (see http://cosmicvariance.com/2008/07/01/what-good-is-a-theory/)
Posted by Lisa Agustin on July 17, 2008 at 10:56 am
Anderson is partly right in that computers have created a field of study sometimes called “experimental mathematics.”
But otherwise, he sounds a little starstruck. Having a lot of data doesn’t do much unless you know what you’re looking for. Anderson’s example of J. Craig Venter’s research sounds impressive, but it doesn’t help Anderson’s thesis. Venter, after all, had a hypothesis and a model and a test. He had a model for the type of data he was looking for, a hypothesis for where to look for it, and a sequencing test to determine if he found it.
So today’s experimentalists have better tools at their disposal than those in the past. Other than that, what’s Anderson’s point?
Posted by Henry Woodbury on July 17, 2008 at 1:43 pm