This habilitation thesis presents research projects that explore how data visualizations can be used to communicate quantitative facts and support decisions. The thesis identifies the umbrella topic of "small data" as the common denominator for these projects, as they all involve datasets that are smaller than what is typically found in the information visualization literature. Although much of this literature focuses on large datasets, this thesis shows that we often do not know how to best visualize even small datasets, and that interesting research questions can also arise from such datasets. The first part of this thesis focuses on supporting rational judgments and decisions with data visualizations. It examines whether data visualization can improve reasoning with base rates, it looks at whether a cognitive bias called the attraction effect transfers to data visualizations, and discusses how to generally evaluate data visualizations for decision making. The second part of this thesis discusses how to support effective communication with data visualizations. It begins by exploring different ways in which researchers can communicate their data to their peers: first through tabular visualizations, and then through multiverse analyses. It then reports on two studies examining how to communicate data to large audiences: a replication study on whether trivial charts can hinder truthful communication, and a study on how to convey data on humanitarian issues. Finally, a concluding chapter brings together several of the research problems discussed here by offering perspectives on how data visualization can support rational decision-making and effective communication on humanitarian issues.