Today we’re reposting our most popular guest post of the year. This essay has garnered a lot of attention and for good reason: it speaks directly to a kind of liberal racism that is endemic to the institutions and professions that see themselves as the good guys in this problem. -db


This past December, most major American news outlets ran a story about police shooting statistics and race. No matter where they were situated on the political spectrum, journalists, pundits, and researchers tried to answer the question: Are American police disproportionately targeting and killing black people? The answers were universally supported by data, statistics, claims of objectivity, and a rhetoric of uncomfortable truths. Their conclusions, however, were all over the map.

Nicholas Kristof writing at The New York Times presented a long list of statistical measures that show racial discrimination is alive and well in America in the first of a five part series “When Whites Just Don’t Get It”. Bill O’Reilly over at Fox News argued the exact opposite was true with his own set of empirical measures in his segment “What Ferguson Protestors Accomplished”. MSNBC, USA Today, and CNN also joined the debate with their own experts and incompatible data projections.

CNN journalist Eric Bradner explains that these contradictory results are a paradox, “Two dramatically different statistics – and they could both be right.” According to Bradner’s “Factcheck”, Kristof builds his conclusions from the Federal Bureau of Investigation database on Supplementary Homicide while O’Reilly’s analysis comes from the Center for Disease Control and Prevention; both are incomplete records of police homicides. According to Bradner, the problem is a result of various definitions of cause of death while in police custody, whether natural, suicide, or homicide. Additionally, there is no incentive for police to self-report their own troubled behavior. He concludes that the different police homicide statistics highlight the importance of the US federal government collecting or mandating the reporting of police shootings. Once all of these cases are verified in a database, they would reveal the “definitive trends in police shootings”. Bradner’s logic shows his trust that more information collected by the government will automatically reveal the truth. Sadly, if this solution to mandate the reporting of police shootings were implemented, it would not eliminate racism in America or even alleviate the debate over whose statistics are correct. There would still be an infinite cycle of analysis, fact checks, and responses.

That is because statistics are a method which require constant choices from the analyst, choices that are ideologically charged. There are a range of mathematically appropriate choices that are selected or overlooked according to the person constructing them. A basic example of this is the use of measures of central tendency: the mean, median, and mode all offer a summary position of the midpoint of a dataset, but depending on the context, one will be better than the others at offering clarity to a situation. This clarity is, of course, always a simplification, skimming the surface of situations. When complexity is erased, the surface is inscribed with the analyst’s view of the world and their beliefs about what is plausible. None of the measures of central tendency are “wrong” either mathematically or realistically, yet they are couched in a discourse of objectivity and reliability, and that makes them a dangerous technology.

Using statistics to talk about racialized police aggression accepts that the truth cannot be found among its victims. This is not to say that the ideological potential hidden within statistical analysis is all “bad”. Statistics were first used as a tool of the state and the ruling elite, yet that does not mean that statistics cannot be used to further a liberatory cause. Their power can move across and through hierarchical power structures (e.g power is circular), and it limits the actions of elites as well as less fortunate people. For an excellent essay on this topic, read Ian Hacking’s (1980) piece “How should we do the history of statistics?”

The demand for statistical proof started as a response to urbanization in 18th century Europe; it was suddenly possible for two individuals living in large cities to never meet or share similar experiences. Theodore Porter in his 1995 book Trust in Numbers explores the history of quantification and statistics in European and American public life. By looking at a diversity of governmental forms (monarchy, democracy, and autocracy) and various academic disciplines (actuarial sciences, political economy, and social engineering), Porter uncovers a common process whereby statistics are adopted as part of “strategies of communication”. Quantification is a “technology of trust” that creates a common language that connects different professions, disciplines, and communities.

Perhaps statistics should be considered a technology of mistrust—statistics are used when personal experience is in doubt because the analyst has no intimate knowledge of it. Statistics are consistently used as a technology of the educated elite to discuss the lower classes and subaltern populations, those individuals that are considered unknowable and untrustworthy of delivering their own accounts of their daily life. A demand for statistical proof is blatant distrust of someone’s lived experience. The very demand for statistical proof is otherizing because it defines the subject as an outsider, not worthy of the benefit of the doubt.

What does this look like in practical terms? A white woman can say that a neighborhood is “sketchy” and most people will smile and nod. She felt unsafe, and we automatically trust her opinion. A black man can tell the world that every day he lives in fear of the police, and suddenly everyone demands statistical evidence to prove that his life experience is real. Anything approaching a “post-racial society” would not require different types of evidence to tell our life stories: anecdotal evidence for white people, statistics for black people. To the media that’s constantly demanding that lived experiences be backed up by statistics, here’s a fact check of your own: Your demand for statistical proof is racist.

Candice Lanius is a PhD student in the Department of Communication and Media at Rensselaer Polytechnic Institute who gets annoyed every time she hears someone say “The data speaks for itself!”


Twitter: @Misclanius