Arman Sabbaghi, Ph.D.

Associate Professor of Statistics at Purdue University

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Teaching Philosophy

It is my firmly-held belief that teaching is a moral enterprise, and that student learning and success should be the primary objectives of teaching. Thus, my teaching philosophy and strategy is guided by the clear declaration of expectations, formulation of learning objectives, adoption of pedagogical innovations, and evaluation of student feedback for continuous improvement of the learning experience.

I am especially dedicated to fostering an appreciation of the applications of statistics in every class that I teach. This objective is accomplished through my adoption of core recommendations in the Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report, which has significantly impacted the teaching of statistics after its endorsement by the American Statistical Association in 2005. Specifically, I promote statistical literacy and thinking, principled data collection and the use of real data sets, and the investigative process of problem-solving and decision-making inherent in statistical applications through group projects and presentations.

I challenge my students, and instill grit and persistence, to learn the fundamental principles and concepts of statistics by focusing on a growth mindset as highlighted by the Stanford University psychologist Dr. Carol Dweck. I encourage my students to reflect on, and draw strength from, their achievements in their course work, highlighting the importance, meaning, and value in what they learn. In my lectures, I emphasize deep conceptual understanding of procedures, as opposed to mere knowledge of the calculations, that is crucial for their successful application, integrate real data with a context and purpose, and perform assessments to continually improve and evaluate student learning.

The accomplishment of student learning and success in statistics has a positive impact because students learn how to apply statistics to address difficult, real-life questions of significance across a wide range of disciplines. On April 9, 2019, I was awarded the Outstanding Assistant Professor Teaching Award by Purdue University's Department of Statistics in recognition of my teaching accomplishments.

Further information on the courses that I have taught can be found in the syllabi below.

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2022 Purdue Krannert-Statistics Causal Machine Learning for Novel Settings Boot Camp

• Introduction to Fundamental Concepts in Causal Inference and ML Approaches of Causal Inference
• Introduction to Fundamental Concepts in Causal Inference and ML Approaches of Causal Inference - Jupyter Notebook
• Lalonde_data.csv
• Lalonde_experiment_data.txt
• Lalonde_observational_data.txt

Fall 2021 Distinguished Seminar Series on Causal Inference

• The Epiphanies of Sir R.A. Fisher and Jerzy Neyman for Causal Inference
• The Epiphanies of Sir R.A. Fisher and Jerzy Neyman for Causal Inference - Jupyter Notebook
• Lalonde_experiment_data.txt

2021 Purdue Krannert-Statistics Machine Learning and Causal Inference Boot Camp

• Introduction to Fundamental Concepts in Causal Inference and ML Approaches of Causal Inference
• Introduction to Fundamental Concepts in Causal Inference and ML Approaches of Causal Inference - Jupyter Notebook
• Lalonde_data.csv
• Lalonde_experiment_data.txt
• Lalonde_observational_data.txt
• airbnb_data_sample_Purdue.csv

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Courses Taught

• STAT 695: Causal Inference Under the Rubin Causal Model (Spring 2025)
• STAT 656: Bayesian Data Analysis (Fall 2022)
• STAT 695: Causal Inference Under the Rubin Causal Model (Spring 2021)
• STAT 515/582: Statistical Consulting and Collaboration (Spring 2021)
• STAT 695: Bayesian Data Analysis (Fall 2020)
• STAT 515: Statistical Consulting Problem (Fall 2020)
• STAT 515/582: Statistical Consulting and Collaboration (Spring 2020)
• STAT 695: Bayesian Data Analysis (Fall 2019)
• STAT 513/IE 530: Statistical Quality Control (Spring 2019)
• STAT 695: Bayesian Data Analysis (Fall 2018)
• STAT 695: Bayesian Data Analysis (Fall 2017)
• STAT 490: Experimental Design (Fall 2016)
• STAT 695: Bayesian Data Analysis (Fall 2016)
• STAT 490: Experimental Design (Fall 2015)
• STAT 513/IE 530: Statistical Quality Control (Spring 2015)