Can you Really Rely on Statistics?

Are people becoming more gullible or are we becoming too lazy to find out the real truth? We learn from a young age that numbers speak the truth, but is that really true or are we being manipulated into believing what others want us to believe? We as people are very opinionated about everything. We all have our own set of views and tend to not deviate from them. We also tend to think research and numbers are reliable and valid.

Based on the textbook Society the Basics 11th edition, reliability is the “consistency in measurement” and validity is “actually measuring exactly what you intend to measure” (John Macionis). When we are given statistics, we believe they are facts but, what we don’t really question is how the data was collected.

There are many ways that data can be collected, represented, and even worded that can affect the outcome. As researchers collect data, they try to avoid being bias but “most researchers realize that we can never be completely value-free or ever aware of all our biases” (John Macionis).

Max Weber stated that “people usually choose value-relevant research topics—topics they care about” (John Macionis). So even from the beginning, researchers are being bias towards the subject they are studying. Although it is hard to not be bias or “value-free” towards a certain outcome, there are procedures and methods that can help like using the double-blind experiment method and other things like random sampling and how to word things that are non-bias.

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I think people are so quick to accept “statistics” as true because with numbers it is straight forward, and people believe you can’t lie with numbers and data. John Macionis states in the textbook that “sociologist use descriptive statistics to state what is “average” for a large population… and the most commonly used descriptive statistics are the mean, the median, and the mode”. Most of our statistics usually comes the United States Sensus Bureau, so we don’t really question it. I do believe that numbers can’t lie, but how the numbers were collected can be false. All data collected is going to have some bias, but it should be minimized as much as possible. Even when collecting data, it can be bias because the people who feel more strongly towards the subject are more likely to answer the questions or get back to you.

A type of bias that researchers try to avoid is the “White hat bias” which is “bias leading to distortion of information in the service of what may be perceived to be righteous ends”. Although we might have the right intentions in mind, it should not have an impact on the research. Most researchers try to avoid having outside sources affect the outcome of their research so that their collected data is reliable and valid. I believe from a scientific point of view, spinning the truth is not acceptable but from a critical point of view it is acceptable especially if it is helping or advancing our environment or everyday life. According to the article “Recognizing “Spin” in the Scientific Literature”, “Spin is a common tool of propaganda often employed by media outlets to push an agenda”. People tend to spin the truth especially in media, to convey a certain point of view or to get a certain reaction from the public.

When collecting data from a scientific point of view, it is not acceptable to spin the truth because science is all about finding out the truth, it’s all about the data. People also tend to rely on science to tell us the facts. Because we are not the ones who are actually studying and collecting the data, we rely on others, the “scientist”, to tell us their findings. When spinning the truth, it is only acceptable from a critical point of view when using it a nonharmful way. I believe it is acceptable because you are not harming anyone and if it is helping society why not? For example, a huge controversial topic is global warming. I believe if spinning the truth a little and helping to show that global warming is true and pollution is changing and having an effect on earth, it is acceptable in that scenario. Nothing is being harmed with a better, cleaner environment.

In social media there are a lot of bias data or conclusions being used to impact our society. One news story that presented bias data and conclusion was the crime rate dramatically dropping from 1991 to 2001. According to the book Freakonomics, people thought that the crime rate dropped because of better police force and the crash of the crack market. The police took the credit for the lower crime rate, but they later found out that it was because of the legalization of abortion on January 22, 1973 due to the ruling in Roe v. Wade. The women who mainly took advantage of Roe v. Wade was single moms who were in poverty or in their teens, women who wouldn’t have been able to properly take care of their child. Children, who would later most likely join gangs or have a criminal record.

To finalize their findings, they collected data on infanticide and the number of babies being put up for adoption. They found out that birth rates fell by 6%, meaning that women were using abortion. In the early 1990s, when the first generation of children born after Roe v. Wade was going into their late teens, “young men entering their criminal prime”, the rate of crime began to fall. Levitt and Dubner noted that “legalized abortion led to less unwantedness; unwantedness leads to high crime rate; legalized abortion, therefore, led to less crime”. The bias that I first saw was when they only looked at people in poverty, single moms, and even young teens. I also noticed that there was also the “white hat bias”, when they concluded that the police force was the reason why the crime rate fell.

People tend to look for the easiest answers the “near-term causes”. They look only at the cause and effect, the more obvious answers instead of taking the time to look for the deeper, truer causation. People are so quick to accept statistics because it is the easy way out. People tend to be lazy and not want to take the time to look at what the data is really saying. We also tend to think that numbers can’t lie to us but, what we don’t take into account is that the researchers who collected the data could be bias and affect the data and true findings. So, when looking at data and statistics we should actually read and look at the data and then consider the reliability and validity of the source.

References

1. Cope, M. B., & Allison, D. B. (2010). White hat bias: examples of its presence in obesity research and a call for renewed commitment to faithfulness in research reporting. International Journal of Obesity, 34(1), 84+. Retrieved from http://link.galegroup.com/apps/doc/A216896520/GPSu=miss22358&sid=GPS&xid=6ae11867
2. Levitt, S. D., & Dubner, S. J. (2015). Freakonomics: A rogue economist explores the hidden side of everything. London: Penguin.
3. Macionis, J. J. (2011). Society: The basics. Boston: Pearson.
4. Organ, J. (2018, May 02). Recognizing ‘Spin’ in the Scientific Literature. Retrieved November 7, 2018, from https://blogs.plos.org/scicomm/2018/05/01/recognizing-spin-in-the- scientific-literature/