At the Harris School of Public Policy, statistics is a foundation for rigorous, high-stakes thinking. In a world awash in data but often lacking clarity, policy professionals must be able to extract credible meaning from complex datasets, distinguish correlation from causation, and communicate uncertainty with precision.

That’s why the statistics sequence is among the most demanding in public policy education. It equips students to interpret evidence, evaluate causal claims, and inform consequential decisions across sectors. Whether assessing program effectiveness, forecasting public health outcomes, or guiding budget priorities, Harris students learn to turn raw data into insight.

Throughout the statistics sequence, students use real-world datasets and code-based tools common to policy work, preparing them not only for second-year courses in program evaluation, data analytics, and machine learning, but for the ambiguity and complexity of real-world decision-making.

What You’ll Learn

Course

Statistics I

Students begin by building core skills to explore and interpret data responsibly. They learn to summarize, visualize, and analyze datasets while avoiding common pitfalls that lead to misinterpretation. Projects involve hands-on coding and data exploration, giving students experience with tools used widely in applied policy analysis.

Equally important, students learn how to quantify and communicate uncertainty. Concepts like confidence intervals, sampling error, and statistical significance are not treated as abstractions—they’re examined in context, using examples like unemployment trends, income distributions, and education outcomes. These tools serve as the groundwork for responsible policy reasoning and evidence-based advising.

Course

Statistics II

The second course in the sequence shifts from describing the world to understanding what changes it. Students engage deeply with causal inference—the cornerstone of credible policy evaluation. Through exposure to techniques such as regression analysis, difference-in-differences, regression discontinuity, and instrumental variables, students learn how to identify cause-and-effect relationships. These methods are essential tools for evaluating whether a program worked, for whom, and under what conditions. Students also develop the judgment to assess the credibility of others’ research, equipping them to synthesize a broader body of evidence and offer well-founded insights.

Eyal Frank
Eyal Frank, Assistant Professor

Learning from Faculty Who Use These Tools

Eyal Frank, Assistant Professor

How do we measure the value of protecting an endangered species—or preserving clean air and water? For Assistant Professor Eyal Frank, an environmental economist, these are empirical questions that statistics can help answer.

Frank’s research examines how environmental policies shape economic and ecological outcomes, including his work on the Endangered Species Act. While critics often argue that endangered species protections reduce property values, Frank’s research finds a more nuanced story—one in which environmental protections can generate broader benefits through improved amenities and long-term value.

In his research and teaching, Frank regularly uses the same statistical tools students learn in the Core. In his current work, he applies regression discontinuity methods taught in Statistics II to estimate the extent to which voters value environmental protections. His work reflects the Core’s central goal: using careful empirical reasoning to inform consequential policy decisions.

Brendon Krall
Brendon Krall, MPP'23

Putting Analysis to Work

Brendon Kall, MPP'23

Brendon Krall came to Harris looking to translate his experience as a classroom teacher into system-level impact. Today, as a Research Project Manager at the Annenberg Institute at Brown University, he helps school districts evaluate what truly works in education.

“At Harris, I learned how to design and analyze rigorous evaluations,” Krall says. “Now I use those same skills to run randomized control trials that inform how educators are trained and supported.”

The connection to the Harris Core is direct. “What I do now is exactly what we practiced—turning data into insight, using evidence to guide decisions, and keeping people at the center of policy design.”