Understanding Significance Tests From a Non-Mixing Markov Chain for Partisan Gerrymandering Claims

Wendy K. Tam Cho, Simon Rubinstein-salzedo

Research output: Contribution to journalArticlepeer-review

Abstract

Recently, Chikina, Frieze, and Pegden proposed a way to assess significance in a Markov chain without requiring that Markov chain to mix. They presented their theorem as a rigorous test for partisan gerrymandering. We clarify that their ε-outlier test is distinct from a traditional global outlier test and does not indicate, as they imply, that a particular electoral map is associated with an extreme level of “partisan unfairness.” In fact, a map could simultaneously be an ε-outlier and have a typical partisan fairness value. That is, their test identifies local outliers but has no power for assessing whether that local outlier is a global outlier. How their specific definition of local outlier is related to a legal gerrymandering claim is unclear given Supreme Court precedent.
Original languageEnglish (US)
Pages (from-to)44-49
Number of pages6
JournalStatistics and Public Policy
Volume6
Issue number1
DOIs
StatePublished - Jan 1 2019

Keywords

  • Markov chain Monte Carlo
  • Redistricting
  • Simulation

ASJC Scopus subject areas

  • Public Administration
  • Statistics and Probability
  • Applied Mathematics
  • Statistics, Probability and Uncertainty

Fingerprint

Dive into the research topics of 'Understanding Significance Tests From a Non-Mixing Markov Chain for Partisan Gerrymandering Claims'. Together they form a unique fingerprint.

Cite this