Max Weber on why there is no decision process for research

In the process of moving, I've come across a bunch of books from my undergrad Sociology minor days, including a book of collected works by Max Weber. You may know him best for the notion of the Protestant work ethic.

At any rate, the volume includes text from a lecture called Science as a Vocation (available free online here), which I've decided to read through because of its personal relevancy, and I've come across this wonderful paragraph.

"Nowadays in circles of youth there is a widespread notion that science has become a problem in calculation, fabricated in laboratories or statistical filing systems just as 'in a factory,' a calculation involving only the cool intellect and not one's 'heart and soul.' First of all, one must say that such comments lack all clarity about what goes on in a factory or in a laboratory. In both, some idea has to occur to someone's mind, and it has to be a correct idea, if one is to accomplish anything worthwhile. And such intuition cannot be forced. It has nothing to do with any cold calculation. Certainly calculation is also an indispensable prerequisite. No sociologist, for instance, should think himself too good, even in his old age, to make tens of thousands of quite trivial computations in his head and perhaps for months at a time. One cannot with impunity try to transfer this task entirely to mechanical assistants if one wishes to figure something, even though the final result is often small indeed. But if no 'idea' occurs to his mind about the direction of his computations and, during his computations, about the bearing of the emergent single results, then even this small result will not be yielded."

This seems to me to be a nice enough refutation, 90 years prescient, of that strange Wired article from a few years ago which claimed that big-data is going to kill the scientific method.

It also resonates with an issue near and dear to my heart: promoting statistical literacy within linguistics. And that takes a two pronged approach. The first is developing statistical competency to be able to run and analyze your own statistics, without relying on semi-automated techniques, like stepwise regression, or put slightly differently, transferring the task entirely to mechanical assistants. The second is to be sure to treat statistical methods as tools for investigation, not to reify them as the objects if inquiry themselves, nor their results as god's truth, spoken by its R-acle.