Gill Vs Whitford Analysis

The most powerful jurists in the country cannot do math. In March 2018, when the Supreme Court of the United States heard oral arguments in Gill v. Whitford, a landmark case that would determine the future of partisan gerrymandering, its members were reluctant to consider statistical evidence seriously.

A concept called the “efficiency gap” lies at the core of Gill v. Whitford: it is a simple fraction where the numerator is the difference between the two parties’ wasted votes and the denominator is the total number of votes cast. During the oral argument, Chief Justice Roberts dismissed the straightforward concept, stating that “[i]t may be simply my educational background, but I can only describe it as sociological gobbledygook.” Justice

Breyer made similar remarks, asserting that “…the hard issue in this case are… standards manageable by a court, not by some group of social science political ex … you know, computer experts.”
While many people view the Justices as unreasonable in their dismissal of the efficiency gap, they reveal a broader concern in today’s legal system: lawyers and judges oftentimes lack the background knowledge to appropriately assess mathematical evidence. As technology rapidly advances, the legal profession’s often hostile relationship with math and science – as exemplified by the ways in which courts admit and misuse statistical evidence – inevitably diminishes the rule of law. At the dawn of the 21st century, Americans face complex legal issues regarding matters like digital privacy and biometric analyses, the fate of which will unavoidably suffer as a result of the legal profession’s dearth of mathematical knowledge.
In the first two parts of this paper, I will show that judges’ lack of quantitative understanding manifests itself in not only the rejection of but also the misuse of statistical evidence. Then, in the latter two parts, I will contend that one of the reasons contributing to this intellectual epidemic lies within the development of America’s legal education system. I will outline the history of legal education from the colonial era to the present and argue that academic institutions’ misleading emphasis on “studying the law as a science” have distracted us from learning and understanding the essence of quantitative reasoning in the context of law.
Part I: The Rejection of Statistical Evidence
In 2010, the Supreme Court’s controversial ruling in Citizens United v. FEC practically allowed corporations to spend unlimited amount of money to support or denounce political candidates. Some argue that the ruling has led to today’s explosion of outside spending in American elections. In response to this new torrent of outside spending, several states – including Arizona, Connecticut, and Maine – strengthened their public funding laws that sought to ensure publicly funded candidates an equal playground as their privately funded opponents. In Arizona, the Clean Election Act provided matching government funding to publicly financed candidates based on the money raised and spent by their privately funded opponents.
The fate of the Clean Election Act was put at stake before the Supreme Court in the 2011 campaign finance case, Arizona Free Enterprise Club’s Freedom Club PAC v. Bennett. Arizona attempted to justify its legislation on grounds of fairness and equality, but the question eventually boiled down to whether providing publicly funded candidates with matching funds burdens political speech of their privately funded opponents. Although an abundance of statistical analyses by multiple scholars showed that private contributors do not alter their behavior in response to matching funds, these social science findings were ultimately dismissed despite the fact that they were heavily submitted to the Court. As a result, the Supreme Court ruled the Clean Election Act unconstitutional, arguing that the state’s anti-corruption interests did not justify the “substantial burden” on free speech. In light of the ruling, many people argue that the decision in Arizona Free Enterprise Club’s Freedom Club PAC v. Bennett has exposed many elections to the influence of big money forces by disincentivizing candidates who are willing to forgo private campaign contributions.
Nowhere in the Court’s analysis did it directly engage with the numerous statistical data presented in front of it. Instead, in his majority opinion, Justice Roberts cites a precedential statement from the Warren Court: “Since as a practical matter it is never easy to prove a negative, it is hardly likely that conclusive factual data could ever be assembled.” What Roberts argues is that no matter how much evidence there is supporting the fact that privately funded candidates are unaffected by the matching provision, one can never definitively prove that none of these candidates is burdened. Robert’s argument in rejecting statistical evidence involves a fundamental mistake called the “negative effect fallacy”, as coined by Harvard Professor Ryan Enos in 2017. The fallacy occurs when one argues that because on a philosophical level it is impossible to prove with absolute certainty that something does not exist, all statistical evidence should be discarded. For instance, one can erroneously argue that no many how many non-flying pigs there are on the planet, their numbers do not prove the nonexistence of pigs that can fly. Nevertheless, Enos points out that the negative effect fallacy is simply an error of language and logic, and it prevents people from using statistical tools that have highly accurate descriptive and predictive powers. In this way, powerful quantitative evidence has been ignored by the Court based on a simple yet detrimental fallacy.
Arizona Free Enterprise Club’s Freedom Club PAC v. Bennett was certainly not the only time the Court blindly rejected statistical evidence. In 2001, although the Court cited statistical data that showed that banning the dissemination of illegally intercepted communications does not deter future illegal interceptions, Justice Rehnquist in his dissenting opinion dismissed the evidence and called it “voodoo statistics”.
At this point, it is important to point out that I am not contending that any of the cases mentioned above was wrongfully decided or that judges ought to make decisions based solely on statistical evidence. I am, however, arguing that it was unreasonable for the Court to think that “it is hardly likely that conclusive factual data could ever be assembled.” This erroneous way of reasoning has allowed the Court to ignore highly relevant empirical evidence and turn to other forms of thinking that do not necessarily reflect the truth.
Harvard law professor Laurence H. Tribe once denounced the use of mathematics at trial, saying that the “overbearing impressiveness” of numbers tends to “dwarf” other evidence. However, in the age of science and technology, quantitative data will play an ever more important role in judicial deliberations. We cannot and should not discard important evidence simply because it seems difficult to comprehend – doing so would impede justice, and in worst cases, destroy innocent lives.
Part II: The Misuse of Statistical Evidence
Although the Court has blindly rejected powerful statistical evidence in recent history, it paradoxically at the same time has acknowledged the importance of such evidence. As statistical methodologies improved and as society’s demand for scientific findings increased, there has been a rapid movement in pursuing quantifiable data in criminal justice. Despite this, the judicial system still struggled to prevent flawed or misguiding statistical evidence out of the court room. A primary example is the rule of admissibility for scientific evidence.
In its 1993 decision in Daubert v. Merrell Dow Pharmaceuticals, the Supreme Court devised a five-factor test to examine the reliability of scientific evidence. The Court wrote that in order for a piece of scientific evidence to be admissible, it must satisfy the following criteria:
• The method employed can be and has been tested
• The results are subjected to peer review and publication
• The known or potential rate of error must be acceptable
• The technique employed must be generally accepted in the scientific community
• The research must be conducted independently without the intent to provide a proposed testimony
Building onto the ruling in Daubert, the Court subsequently decided in General Electric Co.

v. Joiner and Kumho Tire Co. v. Carmichael that the task of “gatekeeping” – or ensuring that expert testimonies, including statistical evidence, are relevant and reliable – belongs to the judge rather than independent experts. Eventually, in 2000, the decisions in Daubert, General Electric, and Kumho served as the basis for the Federal Rule of Evidence 702. Since then, over half of the states have adopted some variation of the so-called Daubert standard.

In light of Rule 702, judges who oftentimes lack a quantitative background took over the responsibility that previously belonged to highly trained experts. Their newly gained role as evaluators of statistical evidence has led to a series of chaos and confusion. The interpretation of the term “peer review”, for instance, has been inconsistent among different courts throughout the country. Professor Paul Giannelli writes in Case Western Reserve Law Review that the “peer review” standard in some courts has been interpreted to simply mean that someone has double-checked a lab analyst’s results rather than a “rigorous peer review with independent external reviewers to validate the accuracy … [and] overall consistency

with scientific norms of practice,” which was Daubert’s intent.
As a result of these inconsistent interpretations of “peer review”, courts have mistakenly cited the prevalence of certain forensic techniques as evidence for their scientific value.

In U.S. v. Havvard, numerous scientific findings challenged the validity and accuracy of the latent fingerprint matching technique. The technique involves using special procedures to uncover finger print residues that are invisible to the naked eye, and studies find that it can vary significantly in its quality and correctness. The court, nevertheless, referred to latent finger print matching as the “archetype” of reliable expert testimony by asserting that it “[has] been used in ‘adversarial testing for roughly 100 years,’ which offered a greater sense of the reliability of fingerprint comparisons than could the mere publication of an article.” Though many studies point out that finger print collection and examination can be highly inaccurate if done without rigor, fingerprinting methods such as latent print matching have not suffered a sustained challenge in federal court in nearly 100 years. In addition to latent print matching, the National Academy of Sciences, the National Commission on Forensic Science, the President’s Council of Advisors on Science, and Technology and the Texas Forensic Science Commission have found that many well-known and admitted forensic science techniques such as bite-mark analysis, microscopic hair comparison, and arson evidence are questioned by independent

studies but are still used in some courtrooms today.