The Mini Test Book (in development)
Python and R in parallel to demonstrate the execution of these tests.
Preface
Have you ever felt overwhelmed by the numerous fundamental hypothesis tests you need to learn in statistical inference courses?
We have experienced this sense of overwhelm throughout our academic journeys as well. However, we also understand that statistical inference is a powerful tool for gaining insights into complex populations across various fields of study. Whether analyzing electoral preferences in political science or assessing the effectiveness of innovative medical treatments in randomized clinical trials, the applications are extensive. Hence, in response to these challenges, we have created this mini-book as a handy resource to help structure and simplify the learning of different fundamental hypothesis tests. Our goal is to present these concepts in a reader-friendly manner while clearly explaining the necessary statistical jargon, making these inferential methods accessible to a broader audience.
Note that, after conducting extensive research into the available educational literature, we discovered that there is no comprehensive and frequentist resource that explains various inferential methods simultaneously using two essential programming languages in the field of data science: R (R Core Team 2024) and Python(Van Rossum and Drake 2009). Furthermore, we could not find reproducible and transparent tools that would enable learners to implement and adapt these methods in their own computational environments. Based on our teaching experience, these shortcomings hinder effective learning in the practice of statistical inference, especially given the numerous tests required to achieve mastery.
To address this gap, we have developed a bilingual and frequentist resource in both R and Python, which features a common test workflow consisting of eight distinct stages applicable to each hypothesis test: study design, data collection and wrangling, exploratory data analysis, testing settings, hypothesis definitions, test flavour and components, inferential conclusions, and storytelling. Additionally, all the tests we discuss are organized through different mind maps to help readers visualize their learning process. Finally, by offering this mini-book as an Open Educational Resource (OER) in Quarto via a GitHub repository, we aim to inspire and empower academic communities worldwide to share and adapt this knowledge to suit their specific needs.
The Authors
G. Alexi Rodríguez-Arelis
I’m an Assistant Professor of Teaching in the Department of Statistics and Master of Data Science at the University of British Columbia (UBC). Throughout my academic and professional journey, I’ve been involved in diverse fields, such as credit risk management, statistical consulting, and data science teaching. My doctoral research in statistics is primarily focused on computer experiments that emulate scientific and engineering systems via Gaussian stochastic processes (i.e., kriging regression). I’m incredibly passionate about teaching regression topics while combining statistical and machine learning contexts.
Ekaterina (Kate) Manskaia
I’m a multidisciplinary professional with a blend of expertise in bioinformatics, computational chemistry, and business analysis. Currently, I’m pursuing a Doctoral degree in Bioinformatics at the University of British Columbia (UBC), where my research focuses on leveraging generative AI and machine learning to uncover new drug candidates for cancer and infectious diseases. I combine these technologies with biostatistical analysis to validate hypotheses and ensure meaningful insights. Alongside my research, I’ve led projects in computational chemistry, managed complex data systems, and mentored students in Python programming, machine learning, and data science. For the past few years, I’ve also been working as a Teaching Assistant in UBC’s Master of Data Science program.
Payman Nickchi
I am a Postdoctoral Research and Teaching Fellow in the Department of Statistics and the Master of Data Science (MDS) program at the University of British Columbia (UBC). I completed my PhD in Statistics at Simon Fraser University (SFU), where my research focused on biostatistics and goodness-of-fit tests using empirical distribution functions. I am currently teaching statistical courses in the MDS program at UBC. My passion for statistics, teaching, and data science led me to this role. Outside of work, I enjoy swimming and capturing the night sky through astrophotography.
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
