Perception of linear and nonlinear trends: using slope and curvature information to make trend discriminations.

This study investigated several factors influencing the perception of nonlinear relationships in time series graphs. To model real-world data, the graphed data represented different underlying trends and included different sample sizes and amounts of variability. Six trends (increasing and decreasing linear, exponential, asymptotic) were presented on four graph types (histogram, line graph, scatterplot, suspended bar graph). The experiment assessed how these factors affect trend discrimination, with the overall goal of judging what types of graphs lead to better discrimination. Six participants (two psychology professors, four psychology graduate students) viewed graphs on a computer screen and identified the underlying trend. All participants were familiar with the types of trends presented and were aware of the purpose of the experiment. Analysis indicated higher accuracy when variability was lower and sample size was higher. Choice accuracy was higher for nonlinear trends and was highest when line graphs were used.