math can – A neuroscientist focused on political reform says simple statistical tools, including Student’s t-test and Monte Carlo simulations, can diagnose when redistricting treats parties differently. He argues the most telling sign of partisan gerrymandering is that a
When redistricting power flips hands, the political map can change fast—sometimes more than voters themselves. One proposal gaining attention centers on treating that shift like a measurable statistical problem, using tests designed to spot when the two major parties are being treated unequally.
In a recent op-ed co-written with Brian Remlinger. Sam Wang argues that math can function as a diagnostic tool rather than a complicated theory.. He compares it to everyday measuring devices. saying it can be used to determine whether the two parties are treated differently.. Of the many options. he points to what he calls a particularly durable test: Student’s t-test. developed nearly a century ago by William Gosset. a Guinness Brewing Company brewer who published under the pseudonym “Student.” Wang says he has lately emphasized the t-test because it targets a specific question—whether one party’s wins are more lopsided than the other’s.
Wang also describes a Monte Carlo approach that starts with chance. then asks what random districting would look like if it followed “accepted districting procedures” rather than a partisan plan.. Monte Carlo sampling. he says. works by making random picks: districts are sampled across the country. in a number equal to the number of districts in a state being studied.. The method then uses accommodations matching the state’s actual vote and counts seats for each party. producing a distribution of outcomes that can be compared to what actually happened.
The discussion of alternative tools comes with a warning of its own.. Wang says more complex standards can create more ways to challenge them. even if they help quantify concepts like wasted votes.. He argues that even if a standard is easier in concept. it can still face scrutiny when it is based on a new idea.
To explain why he keeps returning to simplicity, Wang describes what he considers the direct payoff of the t-test.. The test’s goal. he says. is to determine whether one party’s wins are more lopsided than the other’s—an offense he links to the mechanics of packing and cracking.. He also says multiple tests may be useful because some states have quirks that may require different treatment.
A key graphic in the op-ed shows how the number of House seats gained by partisan gerrymandering doubled between the 2000 and 2010 redistricting cycles.. Wang says that graph comes from the Monte Carlo simulation and represents net gain across all states where one party appears to have arranged an advantage for itself.
He points to a pattern he calls a major tell: the size of the advantage can change suddenly when the party in control changes.. Wang highlights North Carolina’s 2010 redistricting switch. when partisan control moved from Democratic to Republican. saying populations of voters did not move—boundaries did.. In that framing. the conflict is not subtle: if the electorate stays put while the advantage jumps. the mapmakers’ choices are suddenly hard to separate from partisan strategy.
The question of reform expands beyond math, into how the country draws districts in the first place.. Wang references comments tied to his work. saying partisan gerrymandering is made possible by having many districts and that “the more boundaries there are. the easier it is to commit bad acts.” He then asks what would happen if the system shifted toward multi-member districts. meaning fewer districts for politicians to manipulate and more choices for voters.
Wang says such a shift could work. but he adds a specific condition: reform would need at least five members per district to give power to both parties.. Still, he says he believes current national law requires Congressional districts to be single-member.. At state and local levels. he says multi-member districts have been used in the past to suppress minority representation. arguing any reform would have to be designed to avoid repeating that offense.
The thread running through the approach is a consistent sequence: he treats math as diagnosis. uses random simulation to reflect “accepted” redistricting behavior. and then looks for moments when the size of partisan advantage shifts sharply after a change in control—such as in North Carolina in 2010—when voters’ populations were not said to have moved.
partisan gerrymandering redistricting Monte Carlo simulation Student's t-test North Carolina 2010 packing and cracking multi-member districts election fairness