Oral review
Background
One of the learning goals of this class is to:
effectively communicate statistical ideas and results, both verbally and in writing
Whether you choose to stay in academia or pursue a career in industry, the ability to communicate clearly is of paramount importance. As a data analyst, the burden of proof is on you to convince your audience that what you are saying is true. If your audience (who may very well be less knowledgeable about statistics than you are) cannot understand your results or their interpretations, then the technical merit of your project is irrelevant.
We are well aware that AI tools can likely complete this project to a satisfactory degree (see, for example, DeLuca, L. S., Reinhart, A., Weinberg, G., Laudenbach, M., Miller, S., & Brown, D. W. (2025). Developing students’ statistical expertise through writing in the age of AI. Journal of Statistics and Data Science Education, 33(3), 266–278. https://doi.org/https://doi.org/10.1080/26939169.2025.2497547). However, we are not interested in that—we are interested in your ability to discuss your statistical findings in reasonable depth. While you are welcome to use AI tools to prepare your project, we don’t recommend them, and we will conduct an oral review so that we can assess your statistical proficiency in an AI-free environment.
Procedure
You will prepare a Quarto document that contains (at least) the following:
- a bivariate graphical summary of your data (i.e., a scatterplot)
- the output from the primary statistical test that you conducted (i.e.,
summary(lm(...))) - one other thing you’d like to show us
Most students choose to simply use their draft of the technical report. You will hook up one of your laptops to the computer in McConnell 214 and project it for all of us to see.
Be prepared to discuss your data set and your findings orally with us. We will ask you questions that force you to think on-the-fly. The questions will be straightforward—we’re not trying to test your knowledge of statistical esoterica. The questions will also not be about coding. Although we may ask you to code things in the moment, we’ll help you write the code if you need help.
Some of our favorite questions are:
- What does that number mean (in the context of the problem)?
- What would happen if you changed this to that?
- Why did you choose to do this instead of that?
Advice
- DON’T over-prepare. If you actually did the project and learned the material in the class, you shouldn’t have any trouble answering our questions.
- DON’T try to filibuster us by delivering a long, set of prepared remarks about your data set in an attempt to run out the clock. This is not a speech.
- DON’T try to read from a set of notes, a draft of your paper, or other printed materials. Don’t try to read from the Quarto document on the screen, either. We know you can read—we want to see you think on your feet.
- DON’T try to read from other laptops, phones, tablets, or other devices. Be fully present. You will be surprised by how much you remember!