Begin With Bayes: Assessing a Probabilistic and Decision Theoretic Approach to Statistics and Machine Learning Instruction

The framing and depth of statistics and machine learning instruction varies widely. These differences rightly depend on the specifics of the field in which statistics and machine learning is applied, aligning instruction and existing student understanding. However, in settings where students don’t have a common application, statistics and machine learning instruction and existing student understanding can be misaligned. In this paper, we propose using probability and decision theory as a general, unifying approach to statistics and machine learning instruction. We evaluate the impact of this approach on business school student understanding in an interdiscplinary setting.

Project Organization

• /code Scripts with prefixes (e.g., 01_import-data.py, 02_clean-data.py) and functions in /code/src.
• /data Simulated and real data, the latter not pushed.
• /figures PNG images and plots.
• /output Output from model runs, not pushed.
• /presentations Presentation slides.
• /private A catch-all folder for miscellaneous files, not pushed.
• /writing Paper, report, and case studies.
• /.venv Hidden Python project library, not pushed.
• .gitignore Hidden Git instructions file.
• .python-version Hidden Python version file.
• pyproject.toml Python project environment configuration file.
• uv.lock Python project environment lockfile.

Project Environment

After cloning this repository, go to the project’s terminal in Positron and run uv run to create the /.venv project library and install the specified Python and library versions.

For more details on using Python, Positron, GitHub, Quarto, etc. see the recommended data stack.