Graphical Aids to the Estimation and Discrimination of Uncertain Numerical Data

Myeong-Hun Jeong; Matt Duckham; Susanne Bleisch

doi:10.1371/journal.pone.0141271

Graphical Aids to the Estimation and Discrimination of Uncertain Numerical Data

PLoS One. 2015 Oct 27;10(10):e0141271. doi: 10.1371/journal.pone.0141271. eCollection 2015.

Authors

Myeong-Hun Jeong¹, Matt Duckham², Susanne Bleisch³

Affiliations

¹ CyberGIS Center for Advanced Digital and Spatial Studies, University of Illinois at Urbana-Champaign, Urbana, IL, United States of America.
² School of Mathematical and Geospatial Sciences, RMIT University, Melbourne, Victoria, Australia.
³ Institute of Geomatics Engineering, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland.

Abstract

This research investigates the performance of graphical dot arrays designed to make discrimination of relative numerosity as effortless as possible at the same time as making absolute (quantitative) numerosity estimation as effortful as possible. Comparing regular, random, and hybrid (randomized regular) configurations of dots, the results indicate that both random and hybrid configurations reduce absolute numerosity estimation precision, when compared with regular dots arrays. However, discrimination of relative numerosity is significantly more accurate for hybrid dot arrays than for random dot arrays. Similarly, human subjects report significantly lower levels of subjective confidence in judgments when using hybrid dot configurations as compared with regular configurations; and significantly higher levels of subjective confidence as compared with random configurations. These results indicate that data graphics based on the hybrid, randomized-regular configurations of dots are well-suited to applications that require decisions to be based on numerical data in which the absolute quantities are less certain than the relative values. Examples of such applications include decision-making based on the outputs of empirically-based mathematical models, such as health-related policy decisions using data from predictive epidemiological models.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Cognition*
Decision Making*
Epidemiologic Studies*
Humans
Internet
Models, Theoretical*

Grants and funding

This work was supported by Australian Research Council (http://www.arc.gov.au) Discovery Project Grant Number DP120100072. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.