Journal of Data Science

This study investigates how user ability to manipulate plot features affects graphical perception, by extending a previous graphical study (Vanderplas and Hofmann, 2017) with an interactive framework. Similar to the original study, statistical lineups included two target patterns (a linear trend and a clustering pattern), as well as eighteen null plots generated from three different mixture proportions of the combined cluster and trend models. Participants were asked to select two plots that they perceived as ‘most different’, and were able to interact with the graphics by toggling aesthetic features such as cluster coloring, cluster ellipses, linear trendlines, and regression error bands.

We investigate how the use of bullet comparison algorithms and demonstrative evidence may affect juror perceptions of reliability, credibility, and understanding of expert witnesses and presented evidence. The use of statistical methods in forensic science is motivated by a lack of scientific validity and error rate issues present in many forensic analysis methods. We explore what our study says about how this type of forensic evidence is perceived in the courtroom – where individuals unfamiliar with advanced statistical methods are asked to evaluate results in order to assess guilt. In the course of our initial study, we found that individuals overwhelmingly provided high Likert scale ratings in reliability, credibility, and scientificity regardless of experimental condition. This discovery of scale compression - where responses are limited to a few values on a larger scale, despite experimental manipulations - limits statistical modeling but provides opportunities for new experimental manipulations which may improve future studies in this area.