This study examines the claim that individuals misinterpret the mean of a dataset (displayed as a scatterplot) more when the convex hull of the dataset is less representative of the data. In addition, this study also tests whether outliers in the data can predict the magnitude of error that individuals make in interpreting the mean of the dataset. Lastly, the study investigates whether individuals’ interpretations are predicted better by the mean of the convex hull than by the full dataset’s mean. The method used to conduct these investigations is through a survey, followed by several linear regression analyses. Applications of this study include improving the communication of data in economic policy and business contexts, along with broader applications in extending models that heavily rely on agents’ interpretations of information: especially bounded rationality and social norm-based models. The results show that convex hull unrepresentativeness correlates positively with error in mean interpretation; however, that the convex hull mean is not predictive of the interpretations’ direction. Overall, the study contributes to the field of visual information interpretation by investigating the effect of data structure on its interpretation – an unexplored area of research. This is done while initiating the concretization of bounded rationality in economics, by exploring the idea that individuals perceive a general shape of the information presented to them rather than a detailed, full picture. This can lead to misinterpretations whenever the general shape (convex hull) is not representative of the dataset.