Effect of formal methods on statistical education

Determine, via empirical investigation, whether incorporating formal methods—such as dependently typed proofs and machine-checked executable mathematics—improves statistical education outcomes.

Background

The paper argues that formal methods make assumptions explicit and enable executable proofs, potentially benefiting teaching by clarifying concepts and enabling experimentation. However, the authors acknowledge the substantial learning curve and explicitly state that the pedagogical impact is not established.

The open question concerns whether the adoption of formal methods actually improves statistical education, which the authors note requires empirical study to resolve.

References

Whether formal methods improve statistical education remains an open question requiring empirical investigation.

Great expectations: Unifying Statistical Theory and Programming  (2510.09853 - Saul, 10 Oct 2025) in Section 6.2.3 Teaching

The broader claim is a conjecture: once suitable libraries and practices exist, formal tools may help statisticians develop theory more reliably, reuse arguments at a finer level, and teach the architecture of proofs more clearly.

Statistical Theory in the Age of Machine-Assisted Mathematics: Rethinking How Theory Is Made and Taught  (2609.04481 - Coretto, 3 Sep 2026) in Section 1, Introduction