As with all statistical results (inductive conclusions), network “maps” are commonly interpreted as facts (absolutely correct statements, true propositions).
Thus, conclusions drawn from “mapped” data are usually mistaken for scientific facts.
This tendency is probably based on the common presumption that quantitative (numerical) statements are more reliable.
In fact, quantification is no more than replacing impresion and narrative by measurement, and measurement per se is neither precise nor reliable by definition.
An important mediator and a potential source of bias: Relevance to practice, i.e. decision-making—with varying degrees of social economic cultural impact. Possible alternatives for the purpose of data collection—and mapping big data:
- Applicable to policy-making, i.e. “translational”—the procedure of “translation” presents additional logical/mathematical challenge, since translation, by definition, involves induction, generalization.
- Remotely, if at all, associated with policy-making
- Built specifically to inform policy-making