I am a student of a natural science but very interested in philosophy. During my studies, I have noted a perceived difference in how various disciplines approach the explanation of data they obtain. I would roughly separate this in two classes:
top-down: In some disciplines, scientists seem to first try to recognize patterns in the data, then try to find mechanisms which generate similar patterns. I would label this kind of approach a top-down approach, since the identification of structures in the data precedes attempts to explain its origin. Examples are biology and chemistry, or public opinion polling, where various kinds of regression are often the first data analysis. Edit: I expect most black-box machine learning exercises like convolutional neural networks also fall into this category.
bottom-up: In other disciplines, scientists seem to start by shifting well-known building blocks around until their predictions fit the observed data. I would label this a bottom-up approach, since the recreation of the data-generating process often precedes (or scarcely profits from) attempts to understand patterns in the data. Examples are (I would say) hydrogeology, meteorology, or criminology.
Thinking some more, the difference may be the complexity of the systems under investigation. For disciplines in the top-down category, the processes under investigation can often be studied in isolation, for disciplines in the bottom-up category this separation is generally not possible.
Would you agree with such a distinction? Do you know of any work which has explored similar ideas?