In this blog post we provide a short introduction to the emergence of knowledge through visualization.
The goal of Exploratory Data Analysis is to find the best hypothesis which supports the observation of data. The knowledge discovery process is thus considered to be abductive, i.e. given an observation, our explanation has a reasonably good chance to be right according to our current results, knowledge, and intuition, but there might be an unknown number of explanations that can be at least as good as this one. Further studies through visualization and statistical analysis are then necessary to try disproving our explanation in favor of a better one. The explanation may finally be accepted after a couple of experiments that fail at invalidating it. The insights gained may be used to confirm already known results, as well as provide ideas of novel statistical indicators and data descriptors.
The data properties spotted by visual saliences may challenge current hypotheses and raise new questions. The analyst may want to modify the visualization accordingly, to eventually select a picture which clearly reveals an issue, or which supports a hypothesis. The key role of visualization in the emergence of knowledge is emphasized in (Tukey 1977):