What advice do you have for this student who’s in his first year of college and interested in both statistics and political science?

2026-05-01 · Source: Statistical Modeling, Causal Inference, and Social Science · Field: Science & Research — Mathematics & Computational Sciences, Social Sciences & Behavioral Studies, Legal Studies · Depth: Novice, quick

Summary

A first-year statistics major, Joey Jennings, sought advice on combining statistical thinking with political science and legal reasoning, expressing interest in law school. The respondent, a statistician with a background in physics and political science, reflected on their own career path, noting the historical and current opportunities for innovative work in political analytics. They observed that early applications of statistical models in political science often lacked rigor, creating significant openings for students to contribute meaningfully. The respondent emphasized that today's students can engage in innovative work at a much higher level, building upon existing advancements in the field.

Key takeaway

For students considering careers at the intersection of statistics, political science, and law, you should prioritize developing strong coding skills to deeply understand statistical models. Additionally, select your courses based on the instructor's quality rather than just the course description, as effective teaching can significantly enhance your foundational knowledge and practical application abilities.

Key insights

Interdisciplinary studies in statistics, political science, and law offer significant opportunities for innovation.

Principles

• Coding is fundamental for understanding statistical models.
• Teacher quality often outweighs course descriptions.

In practice

• Learn to code rigorously.
• Work on real-world applications.

Topics

• Statistics
• Political Science
• Law School
• Coding
• Bayesian Workflow

Best for: Data Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Statistical Modeling, Causal Inference, and Social Science.