Fig 1.
Traditional scientific method: Hypothesis-based deduction.
The central concept of the traditional scientific method is a falsifiable hypothesis regarding some phenomenon of interest. This hypothesis is to be tested experimentally or computationally. The test results support or refute the hypothesis, triggering a new round of hypothesis formulation and testing.
Fig 2.
Dimension of data-mining–inspired induction.
Data-driven research begins with an untargeted exploration, in which the data speak for themselves. Machine learning extracts patterns from the data, which suggest hypotheses that are to be tested in the lab or computationally.
Fig 3.
Dimension of allochthonous, model-based reasoning.
This mathematical and computational approach is distributed over two realms, which are connected by correspondence rules.
Fig 4.
The traditional hypothesis-based deductive scientific method is expanded into a 3D space that allows for synergistic blends of methods that include data-mining–inspired, inductive knowledge acquisition, and mathematical model-based, allochthonous reasoning.