Teaching and Educational Methods

Enhancing Student Learning in Statistics and Econometrics Through Experiential Teaching Methods

Kedar Kulkarni(a)
(a)Azim Premji University

JEL Codes: JEL Codes: A22, C90, I21
Keywords: Central Limit Theorem, experiential learning, pedagogy, statistical inference, undergraduate economics education

First Published Online: March 25, 2026

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Abstract

Undergraduate students often struggle with the abstract and technical nature of statistical inference, especially in classrooms where prior mathematical exposure varies widely. This article evaluates the impact of experiential teaching techniques introduced in a second-year econometrics course at a liberal arts university in India. I designed two low-cost, intuitive interventions: (1) a classroom game using chocolates to demonstrate the Central Limit Theorem and (2) a video case study from professional cricket to explain hypothesis testing through the Decision Review System (DRS). These methods aimed to build conceptual clarity and bridge the gap between statistical theory and application. Using quiz performance data from three consecutive cohorts—two taught using traditional lectures and one using experiential methods—I estimate the effect of the intervention on student performance. Students in the experiential cohort scored 1.78 points higher on a 10-point quiz, representing a 35 percent improvement over the traditional cohort and a 0.64 standard deviation increase. The gains were particularly large for students with weaker quantitative backgrounds. Qualitative feedback further highlights strong student engagement and positive perceptions of the activities. Overall, the results suggest that simple, contextually grounded interventions can enhance students’ understanding of statistical inference, especially when tailored to diverse learning needs.

About the Authors: Kedar Kulkarni (kedar.kulkarni@apu.edu.in) is an Assistant Professor, School of Arts and Sciences, Azim Premji University, India.

Acknowledgments: The author gratefully acknowledges Anand Shrivastava and Kade Finnoff for their valuable comments during the initial conceptualization and implementation of the methods. The author also thanks the undergraduate economics students at Azim Premji University for their constructive feedback, which helped refine the design and presentation of the activity. The author further thanks the editor and two anonymous reviewers for their constructive and insightful feedback, which substantially improved the quality of this manuscript. This article does not present any conflicts of interest or financial support for the research conducted. The study has been reviewed and approved by the Azim Premji University Institutional Review Board (APU-SAS-2025-07) and found to be in compliance with relevant policies and regulations concerning research involving human subjects. The findings, interpretations, and conclusions expressed in this article are solely those of the author and do not necessarily reflect the views of Azim Premji University. AI Disclosure: Generative AI tools (ChatGPT 5.1) were used to support language editing, grammar correction, and stylistic refinement of the manuscript. AI was also used to assist with cleaning and formatting regression tables for presentation purposes. All substantive intellectual content, data analysis, interpretation of results, and final editorial decisions were performed by the author. The author reviewed, verified, and takes full responsibility for all content in the manuscript.

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