EN CONSTRUCCION

Contraste de enfoques para avanzar en el conocimiento científico a traves de investigación curiosity-driven, data-driven o hypothesis-driven. Ciencia antigua vs Ciencia moderna?

Anderson C. 2008. The end of theory: the data deluge makes the scientific method obsolete. Wired

The scientific method is built around testable hypotheses. These models, for the most part, are systems visualized in the minds of scientists. The models are then tested, and experiments confirm or falsify theoretical models of how the world works. This is the way science has worked for hundreds of years. Scientists are trained to recognize that correlation is not causation, that no conclusions should be drawn simply on the basis of correlation between X and Y (it could just be a coincidence). Instead, you must understand the underlying mechanisms that connect the two. Once you have a model, you can connect the data sets with confidence. Data without a model is just noise. But faced with massive data, this approach to science — hypothesize, model, test — is becoming obsolete.

There is now a better way. Petabytes allow us to say: "Correlation is enough." We can stop looking for models. We can analyze the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot.

The new availability of huge amounts of data, along with the statistical tools to crunch these numbers, offers a whole new way of understanding the world. Correlation supersedes causation, and science can advance even without coherent models, unified theories, or really any mechanistic explanation at all.

“Petabytes allow us to say: “correlation is enough”. We can stop looking for models. We can analyze the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot[. . .] Correlation supersedes causation, and science can advance even without coherent models, unified theories, or really any mechanistic explanation at all”

Mazzocchi F. 2015. Could big data be the end of theory in science?. EMBO Reports. DOI 10.15252/embr.201541001

Is data-driven research a genuine mode of knowledge production, or is it above all a tool to identify potentially useful information?

... the computational approach can be seen as hypothesis generating, in contrast to the hypothesis-testing character of classical science.