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!”
Fact Check: Your Demand for Statistical Proof is Racist | Miscellaneous Conjectures — January 12, 2015
[…] I am very excited to have been published at Cyborgology (an edited blog on technology and society) this morning. Please check it out: http://thesocietypages.org/cyborgology/2015/01/12/fact-check-your-demand-for-statistical-proof-is-ra… […]
Fact Check: Your Demand for Statistical Proof is Racist - Treat Them Better — January 12, 2015
[…] Fact Check: Your Demand for Statistical Proof is Racist […]
Bee — January 12, 2015
I agree that demanding statistical proof for lived experiences of oppression is condescending as hell, but I've found statistics to be enormously helpful when arguing with people who doubt the severity of issues like racism and sexism.
Statistics create a layer of impersonability. Instead of calling someone racist (which just becomes a useless argument about what the person "meant"), stats allow us to focus on social trends. When I argue about patriarchy in the workplace with some dudebro, for instance, talking about the discomfort I feel under the gaze of male colleagues feels insignificant even to my ears; the stats I can provide on the wage gap, however, allow me to communicate my personal experiences of oppression through the filter of evidence.
I pretty firmly believe that the person who tends to lose an argument is the one who gets more emotional. We're trained to see emotions as weakness, so when we get caught up in our feelings instead of our facts, we begin stumbling into the land of personal anecdotes and reduced credibility. Trying to bully people by delegitimizing their testimony by constantly demanding evidence is oppressive as hell, but at least in my own experience, statistics can be used as armor against those who would like to tell you your story doesn't matter.
Candice Lanius — January 13, 2015
(A pertinent reply to a comment on my personal blog: clanius.wordpress.com )
Thank you for your comment. I appreciate the opportunity to discuss my work.
As to your points: my article is directed towards how statistics are often abused as a communication strategy. I am in no way suggesting that statistics are bad or not useful for scientific investigation. This is like equating a caution that “reliance on fossil fuels hurts the environment” with “all fossil fuels are bad and their use must be stopped immediately”. By discussing the problems with the use of statistics, I am hoping to improve both their use by the media AND create stronger statistical literacy, not call for an immediate end to their application in research.
Statistics are a technology of mistrust that is used when there is no other basis for knowing or the intimate situation is subject to heightened scrutiny. I agree, it is the analysts responsibility in many settings to distrust intimate knowledge. The issue here is a difference in how that distrust and suspicion is applied by the media. The media tend to accept anecdotal evidence from white individuals (whom they trust) and demand statistics to validate a black perspective (whom they don’t trust). Why is this a problem? It is a problem because anecdotes are much easier to mobilize while statistical evidence takes time, money, and a great deal of knowledge to prepare. The demand for statistical proof becomes a silencing tactic.
You mention lying with statistics as an “old observation” that is not “damning of the approach”. I agree- just because statistics can be manipulated as a method does not mean that statistics should never be used. The new question which I raise in my article, however, is the disparate and racially charged demand for statistical evidence in the media. Critical discussions of how statistics are used in public life and democracy are, I believe, always important.
Next, you mention that my words are inflammatory/ hyperbole which suggests bias. Yes. So what? I use hyperbole in an entertaining and evocative blog post which is designed to get people thinking and discussing the issue. My bias is towards interest and action. And there is no such thing as “not having bias”, the question is only how much your bias and my bias overlap.
Finally, you misunderstand my call to action. I am not saying we should get rid of statistics. I am saying we need to understand how different types of evidence and perspectives are discussed and demanded in the news.
Adam — January 14, 2015
I'm a bit confused, because reading the author's article, and the comments, I can't tell if this is meant to be a criticism of media, or a criticism of people.
I agree, it's ridiculous when people instinctively trust the privileged, and instinctively mistrust the oppressed. If a conversation is about a person's lived experiences, asking that person to supply statistics is absurd.
But shouldn't we expect--better yet, demand--the news to be skeptical of everyone's claims? I don't think good journalism was ever based on giving people the benefit of the doubt.
I want to see statistics in media. I want to see *more* statistics. I want to see journalists compare multiple sets of statistics, explain how the data was collected, and discuss the shortcomings of each method.
The media are the gatekeepers of information. If there's one group of people I want to see vetting information, so that we don't have to rely on anecdotal evidence to learn about the world, it's them.
If anything, I'd be much more concerned about the media trusting a racist old white lady because she said my neighborhood was sketchy. I find that a lot more unsettling than a reporter using data about millions of people to more accurately illustrate how something affects people on a scale of millions.
The double standard of letting some people use anecdotal evidence and demanding that some people offer statistics is oppressive. But if we insisted the news rely more on anecdotal evidence we wouldn't have news; we'd have 24 hours of This American Life.
Emily McOll — January 15, 2015
Hey Dr. Lainus,
Loved your insight into differences of "truth" that come from when a white woman speaks versus when a man of colour speaks. I'm looking to do some research on the differing oppression/privilege that white women and men of colour respectively face, and I was wondering if you could point me in the direction of some scholarly articles that could shed some light on the matter. I would really appreciate it!
MC — January 15, 2015
Ethical statistics will always include a detailed definition of what is being described by the numbers. Too often statistics are separated from reports and stand alone as numbers. It is important to know everything about the data: from how it is collected, maintained, and analyzed.
I work professionally as a data analyst. When my department works on a review or report of some type we work with individuals inside the group we will by analyzing. We learn what is appropriate to do with the data and exactly what questions need to be answered. A final report will include qualitative analysis from insiders and our quantitative analysis.
Quantitative and qualitative analyses serve different functions. Experts in both areas deserve a seat at the table. Saying that asking for statistical analysis is racist is not giving a clear picture of what is going on. We should be talking about how to appropriately apply quantitative analysis.
When I ask for statistics it's because 1. I understand the methods and can use the information and 2. I want to know more about the issue and statistics can most certainly add valuable information if conducted ethically.
I don't agree with you when you say: "Using statistics to talk about racialized police aggression accepts that the truth cannot be found among its victims." This exposes a tricky point: there can be many definitions of what the "truth" of racialized police aggression is. Is the personal experience of one individual a truth about this aggression? Yes. Is ethically conducted statistical analysis a truth about this aggression? Yes. Using individual and population level data in decision making are not mutually exclusive.
The mismeasurement of humans: classification as “othering” | Freewheel Burning — January 16, 2015
[…] was part of a short, but interesting discussion last night regarding this very good article on the political implications of data analysis. The argument made (assuming I understood it […]
Comradde PhysioProffe — January 16, 2015
Excellent post. Watch Fox News for a randomly selected half hour, and the double standard of proof you point out will manifest multiple times.
Ellie Kesselman — January 22, 2015
I understand the point being made. Of course it is racist to assign different levels of credibility based on race and gender. This is valid but makes me uneasy:
"Statistics are a technology of mistrust that is used when there is no other basis for knowing or the intimate situation is subject to heightened scrutiny."
When read carefully, it makes sense. There are better ways of expressing this than what seems like a sweeping indictment of statistical inference though. Finding the truth is difficult and sometimes impossible in any consistent way due to varying externalities.
I'd rather describe the problem as differing standards of proof, instead of portraying quantitative analysis so negatively. I am biased though, as I work as a statistician.
Ivan — March 21, 2015
"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."
No, mandating the reporting of police shootings probably will not "...automatically reveal the truth.." But are you seriously arguing that we should not mandate the reporting of police shootings? The need for this information is crucial as a data-set for any genuine effort to get to an understanding of the full reality of an issue, a full reality and can't be gotten close to with anecdotal evidence alone.
Only the people who are determined to be racist, for various reasons, will continue "...an infinite cycle of analysis, fact checks, and responses." They are beyond help. But others need statistics because they really do want to know what the full reality is, which will help them "...eliminate racism in America ..." And they aren't concerned with "...alleviat[jng] the debate over whose statistics are correct." Mitigating or eliminating institutional racism in America requires statistics so that we know when police homicides for whites are commensurate with blacks, so that we know when unemployment for whites is commensurate with blacks, so that we know when college admissions for whites are commensurate with blacks, so that we know when home ownership for whites is commensurate with blacks, so that we know when incarceration rates for whites are commensurate with blacks, so that we know when arrests and police contact for whites is commensurate with blacks, so that we know when ....[fill-in the blank]... for whites are commensurate with blacks.
The only way to change the apparent fact that "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.", is to use statistics to change the balance of societal power between whites and blacks. The white woman in the above hypothetical has her perspective unchallenged because she represents a class with enough societal power because the above statistics are a long way in favor of the class she represents.
What is "sketchy" for sure, is any large-scale solution based on anecdata alone.
My Blog — March 28, 2015
[…] Fact Check: Your Demand for Statistical Proof is Racist » Cyborgology Format QuotePosted on January 13, 2015Author admin […]