%0 Journal Article
%T Understanding Significance Tests From a Non-Mixing Markov Chain for Partisan Gerrymandering Claims
%A Simon Rubinstein-Salzedo
%A Wendy K. Tam Cho
%J Statistics and Public Policy
%D 2019
%R https://doi.org/10.1080/2330443X.2019.1574687
%X 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 ¦Ĺ-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 ˇ°partisan unfairness.ˇ± In fact, a map could simultaneously be an ¦Ĺ-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
%U https://www.tandfonline.com/doi/full/10.1080/2330443X.2019.1574687