[Retric]

A huge range of things are worth doing without being science. Further, just looking at vast amounts of data is not the scientific method.

It's like using vast amount of information to create a self driving car vs. actually letting the car run on real roads. The first can only tell if it approximates reasonable driving, the second can tell if it avoids getting into dangerous situations. You can collect a lot of information on the US economy, but in the real world the FED is actively trying to manage things and you can remove that factor from the data.

[imh]

Where is anyone proposing just looking at vast amounts of data? With certain kinds of data, you can still learn causal effects observationally, like in econometrics. That's the closest I can find in this discussion. I mean, you're totally right naive data analysis is bad and more data doesn't help that, but nobody is advocating for doing that.

[Retric]

You only have two choices, look at data you don't control or data you do. The entire point of experiments is to narrow the range of uncontrolled data as much as possible. Looking at raw data does not help. Looking at huge data-sets of minimally controlled experimental data does not help.

Physicists's for example can't change the age of the universe they are operating in. It's a rather large unknown, but not exactly an unknown unkown.

At the other end, people trust surveys of eating habits. I don't care if you send out a billion of those things it's still bad data in systematic and changing ways.

In between, most animal studies in mice are looking at disease analog X, in a population of fat, minimally stimulated, etc.