2.6 Misusing Crime Statistics
According to the FBI, a violent crime occurred in the United States every 24.6 seconds in 2017. There was one murder every 30.5 minutes, one rape every 3.9 minutes, one robbery every 1.7 minutes, and one aggravated assault every 39 seconds. During the same year, a property crime occurred every 4.1 seconds. There was also one burglary every 4.1 seconds, one larceny-theft every 5.7 seconds, and one motor vehicle theft every 40.9 seconds (FBI, 2017).
The National Coalition Against Domestic Violence (NCADV) reports more than 10 million adults experience intimate partner violence each year, which equates to an incident of abuse occurring at least every 3 seconds. Furthermore, one in four women and one in ten men will experience intimate partner violence during their lifetime. Also, one in two female murder victims are killed by intimate partners, and 96% of all murder-suicide victims are female (NCADV, n.d.). These examples of crime statistics from the UCR and the NCADV show how data from the collections discussed in this chapter may be used and help paint a picture of what is happening in the United States in terms of specific types of crime.
However, while crime statistics can be used for many legitimate purposes, they can also be misused. In each of the examples in figure 2.18, someone is likely to have bias or even ulterior motives (hidden, often selfish reasons) for wanting to use crime stats. Let’s look at these examples with an eye toward the potential for misuse of data.
| Reason Crime Stats Used | Example of Misuse of Data | Ulterior Motive/Bias |
|---|---|---|
| Real estate values in a neighborhood are affected by crime rates. | A real estate agent claims a house is in a “safe” neighborhood by sharing stats on murder (“There hasn’t been a murder here in 3 years!”) while leaving out stats on home burglaries. | Selling a property quickly and at the highest price possible leads to a nice commission for the real estate agent. |
| Tourism is greater where people feel safer. | A city’s tourism office advertises that there is “no crime here!” but fails to reveal that they are only talking about a small area of town that is actually crime-free. | It is their job to increase tourism in their city, so their goal is to get people to visit and spend money in local businesses. |
| Claims about public safety can impact voting. | Someone who is running for office and trying to oust the incumbent cites scary crime rates in the area, picking only the ones that sound really bad, and claims their policies will make the community safe if they are elected. | They want to get elected to public office. |
| The staffing of law enforcement agencies depends on need. | A law enforcement agency shares only those stats that show increases in crime over a carefully chosen period of time and argues they need more officers because of rising crime rates in the area. | Agencies want more money to hire more officers, so they are trying to demonstrate a need. |
| Sales of security systems and guns are linked to crime rates. | Advertisements focus on the scariest crime stats and highlight particularly gruesome examples to make people feel afraid and as though they must take extra steps to protect their families and property. | More sales equals more money for the retailers of security systems and guns. |
| Self-defense classes are more popular when people do not feel safe. | Advertisements warn of impending attacks and how we must be prepared to defend ourselves. | More class registrations mean more money and job security for the instructors. |
| Grant funding depends on demonstration of need. | A researcher shares certain crime stats from a specific time period to show there is a need for their intervention. | They want to get their research funded, so they are trying to show its importance. |
| Racist ideologies and policies gain traction when people are scared. | A white nationalist group claims crime rates are linked to race, arguing (incorrectly and without attention to relevant factors) that some races are more dangerous than others and pushes for segregation by race/ethnicity for security purposes. | They want to form areas where only white people can live. |
As you can see from these examples, some misuses of crime statistics are relatively innocent, and others are downright deceitful and disgusting. Misuse can include providing only limited information, presenting incorrect statistics, and presenting data in a way that is deceiving. It is important to be aware of and to consider the possible motives of the source sharing crime statistics. This will help you determine the accuracy and trustworthiness of the data. While one person’s motive may be pure, another person’s motive may be manipulative, selfish, or discriminatory.
Learn More: The Myth of the Super-Predator
Dr. John DiLulio became famous as a criminologist and political scientist, but for a very bad reason. In 1995, he misused data from a study in Pennsylvania to predict an impending rise in crime and violence among teenagers, especially Black boys. He claimed, “the next ten years will unleash an army of young male predatory street criminals who will make even the leaders of the Bloods and Crips—known as O.G.s, for ‘original gangsters’—look tame by comparison” (DiLulio, 1995, para. 19). Dr. DiLulio said there was a whole generation of heartless, evil, violent kids living in “moral poverty” who were going to terrorize every community (figure 2.19). In particular, he suggested that young Black men were going to be coming after white adults.
He called these new scary teens “super-predators” and convinced a trusting public that they should all be terrified of these youths who were marked by “the impulsive violence, the vacant stares and smiles, and the remorseless eyes” (DiLulio, 1995, para. 6). Legislators latched onto his claim. It backed their tough-on-crime rhetoric and allowed them to pass all sorts of new laws that punished juveniles who committed offenses with longer, harsher sentences, including life in prison without the possibility of parole. DiLulio (1995) warned,
On the horizon, therefore, are tens of thousands of severely morally impoverished juvenile super-predators. They are perfectly capable of committing the most heinous acts of physical violence for the most trivial reasons (for example, a perception of slight disrespect or the accident of being in their path). They fear neither the stigma of arrest nor the pain of imprisonment. They live by the meanest code of the meanest streets, a code that reinforces rather than restrains their violent, hair-trigger mentality. In prison or out, the things that super-predators get by their criminal behavior—sex, drugs, money—are their own immediate rewards. Nothing else matters to them. So for as long as their youthful energies hold out, they will do what comes “naturally”: murder, rape, rob, assault, burglarize, deal deadly drugs, and get high. (para. 29)
Dr. Dilulio’s claim was busted when crime among juveniles did not behave as he predicted. In fact, it went down a lot. Although Dr. Dilulio has since come out and publicly expressed regret, the impact of his misuse of data has been lasting and incredibly harmful. Thousands of teenagers have been negatively impacted by his inaccurate prediction. Furthermore, although he agrees he was wrong, he takes no responsibility for the harsh punishments of children that were the result of his false prophecy. If you would like to learn more, you can read the article Analysis: How the media created a ‘superpredator’ myth that harmed a generation of Black youth [Website].
Accusations based on the super-predator myth still come up in political debates and discussions about criminal justice reform, making this a legendary example of data misuse in criminology. However, this is just one example of how the misinterpretation of crime data can go horribly wrong.
Check Your Knowledge
Licenses and Attributions for Misusing Crime Statistics
Open Content, Original
“Misusing Crime Statistics” by Taryn VanderPyl is licensed under CC BY 4.0. Revised by Jessica René Peterson.
Figure 2.18. “Reason crime stats are used, example of misuse of data, and potential ulterior motive/bias” by Taryn VanderPyl is licensed under CC BY 4.0. Revised by Jessica René Peterson.
“Misusing Crime Statistics Question Set” was created by ChatGPT and is not subject to copyright. Edits for relevance, alignment, and meaningful answer feedback by Colleen Sanders are licensed under CC BY 4.0.
Open Content, Shared Previously
Figure 2.19. Photo is licensed under the Piqsels Terms of Service.
