Statistical Intuition Without Coding (or Teachers)
Natalie Ayers, Gary King, Zagreb Mukerjee, Dominic Skinnion. 2025.
"Statistical Intuition Without Coding (or Teachers)".
PS: Political Science & Politics, 58, 4, Pp. 730–736.

Abstract
Two features of quantitative political methodology make teaching and learning especially difficult: (1) Each new concept in probability, statistics, and inference builds on all previous (and sometimes all other relevant) concepts; and (2) motivating substantively oriented students, by teaching these abstract theories simultaneously with the practical details of a statistical programming language (such as R), makes learning each subject harder. We address both problems through a new type of automated teaching tool that helps students see the big theoretical picture and all its separate parts at the same time without having to simultaneously learn to program. This tool, which we make available via one click in a web browser, can be used in a traditional methods class, but is also designed to work without instructor supervision.
See Also
- [Paper] If a Statistical Model Predicts That Common Events Should Occur Only Once in 10,000 Elections, Maybe It's the Wrong Model (2025)
- [Paper] The Essential Role of Statistical Inference in Evaluating Electoral Systems: A Response to DeFord et Al. (2023)
- [Paper] A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results (2021)
- [Paper] A Theory of Statistical Inference for Matching Methods in Causal Research (2019)
- [Paper] Causal Inference Without Balance Checking: Coarsened Exact Matching (2012)
- [Paper] Statistical Security for Social Security (2012)
- [Presentation] Matching for Causal Inference Without Balance Checking (2009)
- [Paper] How Not to Lie Without Statistics (2008)