This three-article dissertation considers the pedagogical practices for developing statistically literate students and teaching data-driven decision-making with the goal of preparing students for civic engagement and improving student achievement. The first article discusses a critical review of the literature on data-driven decision-making project conditions in K-12 educational settings. Upon reviewing the literature, I synthesized and summarized the current practices into three distinct models. The models serve to clarify the pedagogical choices of the teacher and the degree at which students' views are involved and incorporated into the projects. I propose an alternative model/framework and discuss possible implications in the classroom. In the second article, I use the framework developed in the first article as the basis for an educational research intervention. I describe a study where I developed a handbook based on the framework and implemented a sample of professional development sessions from the handbook. Advisors and teachers provided feedback on the handbook and professional development. This feedback served as the subject of analysis while I continued to refine the handbook and the professional learning sessions. I describe the refinement process and the implications in terms of design decisions of educational interventions and statistical knowledge for teaching. The final article performs a secondary data analysis of school, teacher, and student level data using the Trends in International Mathematics and Science Study (TIMSS) database. The paper seeks to answer the research question: “Which aspects of teacher professional knowledge measures predict student achievement in the mathematical domain of data and statistical topics?” The results indicate that when controlling for school level wealth index, teacher characteristics are not as influential as the school level wealth index. I discuss future research as well as school policy and curriculum implications of these results.