Effect of Human Factors on Visual Statistical Inference

Visual statistical inference determines the significance of patterns found in data exploration through graphics. It involves human observers inspecting a lineup of plots, with one real data plot randomly placed among decoys. Each observer's cognitive skills and judiciousness can influence results. The effectiveness of this method, measured by power, depends on combining evaluations from multiple observers. Human factors influencing power, as computed by the number of detections or identifications of an observed data plot in a lineup, include observer demographics, individual skills, and experience. This paper examines these factors through studies using Amazon's Mechanical Turk, finding individual skills vary but demographics have little impact. Learning increases speed but not accuracy. This article is categorized under:Statistical Learning and Exploratory Methods of the Data Sciences > Exploratory Data AnalysisStatistical and Graphical Methods of Data Analysis > Statistical Graphics and VisualizationStatistical and Graphical Methods of Data Analysis > Nonparametric Methods Statistical Learning and Exploratory Methods of the Data Sciences > Exploratory Data Analysis Statistical and Graphical Methods of Data Analysis > Statistical Graphics and Visualization Statistical and Graphical Methods of Data Analysis > Nonparametric Methods