Data driven science revisited

Chris Anderson once infamously wrote The data deluge makes the scientific method obsolete, an opinion that I do not share. Eric Drexler on the other hand comes at this new age of data driven science with the right mindset. In a post on data explosion and the scientific method, Eric writes

Tradition demands that science always be hypothesis-driven: First, try to guess the truth, and only afterward collect experimental data to test whether the guess predicts the results. Indeed, this has been termed “The Scientific Method”. The new data-driven approach suggests that we collect data first, then see what it tells us. This becomes practical when experimental methods can amass enormous amounts of data, enough data to test more hypotheses than any mortal scientist could conceivably imagine.

The thing that is important here is that testing hypotheses doesn’t go away. In fact, as Eric points out, in some ways this is no different from what we’ve done in some sciences for a long time; Eric mentions astronomers and microsocopists. Darwin’s work also falls into this category. In other words, science has long been based on observations, with those observations leading to hypotheses. The difference now is the sheer speed and volume at which data is collected, overwhelming our old pattern recognition tools, our eyes and mind. To help us make sense of those observations we need machines, but to do good science, we still need to develop models and theorems and then test them. As Eric writes in talking about data driven biology

For these methods to work, we must know enough about patterns (repetition, correlation, difference, functional correspondence…) that we can recognize some of them and separate the real patterns from the statistical illusions. This too is a hypothesis, but there is no pretense of vast insight.

We should not forget that

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