Author Archives: Robin Lock

About Robin Lock

Robin Lock ( is the Burry Professor of Statistics in the Department of Math, Computer Science, and Statistics, St. Lawrence University, Canton, NY

Assessing Knowledge and Understanding of Simulation-Based Inference With Technology

Robin Lock, Burry Professor of Statistics, St. Lawrence University

I have the luxury of teaching in a computer classroom with 28 workstations that are embedded in desks with glass tops to show the monitor below the work surface.  This setup has several advantages (in addition to enforcing max class size cap of 28) since computing is readily available to use at any point in class, yet I can easily see all of the students, they can see me (no peeking around monitors), and they still have a nice big flat surface to spread out notes, handouts and, occasionally a text book (although many students now use an e-version of the text).  I also have software on the instructor’s station (Smart Sync) that shows a thumbnail view of what’s on all student screens.  Since the class is setup to use technology whenever needed and appropriate, it is natural to extend this to quizzes and exams, so my students routinely expect to use software as part of those activities.

[pullquote]Ideally I’d like to see what each student produces on the screen and how they interpret the output to make statistical conclusions, but it’s not practical to look over everyone’s shoulder as they work.[/pullquote] Continue reading

How did I get started on teaching simulation-based inference?

Robin Lock, St Lawrence University

Around 1998, Allan Rossman and Beth Chance asked me to help out with a new edition of their popular Workshop Statistics book that would be adapted to use a new software package called Fathom that was being developed by Bill Finzer, then at KCP Technologies. [pullquote]But I could detect light bulbs going on with students thinking, “Oh, that’s what he means by seeing what would happen if the null hypothesis is true!'[/pullquote] Fathom has a lot of neat tools designed to allow students to explore statistical concepts, including a facility to allow students to easily select a sample from a dataset, define any statistic for that sample, and then quickly generate a new dataset with values of that statistic for many new samples. Continue reading