Graph-based inductive reasoning

Abstract

This article discusses methods of inductive inferences that are methods of visualizations designed in such a way that the “eye” can be employed as a reliable tool for judgment. The term “eye” is used as a stand-in for visual cognition and perceptual processing. In this paper “meaningfulness” has a particular meaning, namely accuracy, which is closeness to truth. Accuracy consists of precision and unbiasedness. Precision is dealt with by statistical methods, but for unbiasedness one needs expert judgment. The common view at the beginning of the twentieth century was to make the most efficient use of this kind of judgment by representing the data in shapes and forms in such a way that the “eye” can function as a reliable judge to reduce bias. The need for judgment of the “eye” is even more necessary when the background conditions of the observations are heterogeneous. Statistical procedures require a certain minimal level of homogeneity, but the “eye” does not. The “eye” is an adequate tool for assessing topological similarities when, due to heterogeneity of the data, metric assessment is not possible. In fact, graphical assessments precedes measurement, or to put it more forcefully, the graphic method is a necessary prerequisite for measurement.