I´d say we set up models in an attempt to overcome our cognitive limitations. Models are not always a faithful representation of the objective reality and they always have to be adjusted to a particular experiment. If we could see natural processes ocurring in a proper speed, deduction - which we use to make models among other things - would have a less important role in the SM.

For instance, we make a model of cloud formation, which explains and predict the formation of clouds in the sky [a very slow process to the human eye]. We deduce the cloud formation from a set of more basic information, such as moisture content of the air, wind speed, temperature and other parameters. But when you make a stop-motion movie of clouds in a given day, and puts it to run, deduction seems to be less necessary to understand the phenomenon. The movie shows the correlations between the parameters arising before our very eyes, and deduction loses some of its weight.

So, I think the ability to mine datasets in a very speedy way, where correlations arise and provide answers to our questioning, is another kind of method, which could be useful in certain areas of research. So it would be fair to say that 'correlational methods' do not substitute the scientific method, for establishing correlations from data sets is in itself an experimental process - a part of the scientific method. They enhance the scientific method rather than substitute it.

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