An Undergraduate Course on the Statistical Principles of Research Study Design

TL;DR

This paper presents a comprehensive undergraduate course on the statistical principles of research study design, emphasizing practical understanding of various study types and fostering critical analysis of real research studies.

Contribution

It introduces a flexible, broad-based curriculum that integrates survey sampling, observational studies, and causal inference, tailored for diverse student backgrounds.

Findings

Students develop improved statistical literacy and communication skills.

The course enhances understanding of research design and critical evaluation of scientific studies.

It serves as an effective bridge to advanced statistical coursework.

Abstract

The undergraduate curriculum in statistics and data science is undergoing changes to accommodate new methods, newly interested students, and the changing role of statistics in society. Because of this, it is more important than ever that students understand the role of study design and how to formulate meaningful scientific and statistical research questions. While the traditional Design of Experiments course is still extremely valuable for students heading to industry and research careers, a broader study design course that incorporates survey sampling, observational studies, and the basics of causal inference with randomized experiment design is particularly useful for students with a wide range of applied interests. Here, I describe such a course at a small liberal arts college, along with ways to adapt it to meet different student and instructor background and interests. The course serves as a valuable bridge to advanced statistical coursework, meets key statistical literacy and communication learning goals, and can be tailored to the desired level of computational and mathematical fluency. Through reading, discussing, and critiquing actual published research studies, students learn that statistics is a living discipline with real consequences and become better consumers and producers of scientific research and data-driven insights.