Telling the Truth About Damned Lies and Statistics
THE REVIEW
Telling the Truth About Damned Lies and Statistics
By Joel Best
MAY 4, 2001
The dissertation prospectus began by quoting a statistic -- a “grabber” meant to capture the reader’s attention. The graduate student who wrote this prospectus undoubtedly wanted to seem scholarly to the professors who would read it; they would be supervising the proposed research. And what could be more scholarly than a nice, authoritative statistic, quoted from a professional journal in the student’s field?
So the prospectus began with this (carefully footnoted) quotation: “Every year since 1950, the number of American children gunned down has doubled.” I had been invited to serve on the student’s dissertation committee. When I read the quotation, I assumed the student had made an error in copying it. I went to the library and looked up the article the student had cited. There, in the journal’s 1995 volume, was exactly the same sentence: “Every year since 1950, the number of American children gunned down has doubled.”
This quotation is my nomination for a dubious distinction: I think it may be the worst -- that is, the most inaccurate -- social statistic ever.
What makes this statistic so bad? Just for the sake of argument, let’s assume that “the number of American children gunned down” in 1950 was one. If the number doubled each year, there must have been two children gunned down in 1951, four in 1952, eight in 1953, and so on. By 1960, the number would have been 1,024. By 1965, it would have been 32,768 (in 1965, the F.B.I. identified only 9,960 criminal homicides in the entire country, including adult as well as child victims). By 1970, the number would have passed one million; by 1980, one billion (more than four times the total U.S. population in that year). Only three years later, in 1983, the number of American children gunned down would have been 8.6 billion (nearly twice the earth’s population at the time). Another milestone would have been passed in 1987, when the number of gunned-down American children (137 billion) would have surpassed the best estimates for the total human population throughout history (110 billion). By 1995, when the article was published, the annual number of victims would have been over 35 trillion -- a really big number, of a magnitude you rarely encounter outside economics or astronomy.
Thus my nomination: estimating the number of American child gunshot victims in 1995 at 35 trillion must be as far off -- as hilariously, wildly wrong -- as a social statistic can be. (If anyone spots a more inaccurate social statistic, I’d love to hear about it.)
Where did the article’s author get this statistic? I wrote the author, who responded that the statistic came from the Children’s Defense Fund, a well-known advocacy group for children. The C.D.F.'s The State of America’s Children Yearbook 1994 does state: “The number of American children killed each year by guns has doubled since 1950.” Note the difference in the wording -- the C.D.F. claimed there were twice as many deaths in 1994 as in 1950; the article’s author reworded that claim and created a very different meaning.
It is worth examining the history of this statistic. It began with the C.D.F. noting that child gunshot deaths had doubled from 1950 to 1994. This is not quite as dramatic an increase as it might seem. Remember that the U.S. population also rose throughout this period; in fact, it grew about 73 percent -- or nearly double. Therefore, we might expect all sorts of things -- including the number of child gunshot deaths -- to increase, to nearly double, just because the population grew. Before we can decide whether twice as many deaths indicates that things are getting worse, we’d have to know more. The C.D.F. statistic raises other issues as well: Where did the statistic come from? Who counts child gunshot deaths, and how? What is meant by a “child” (some C.D.F. statistics about violence include everyone under age 25)? What is meant by “killed by guns” (gunshot-death statistics often include suicides and accidents, as well as homicides)? But people rarely ask questions of this sort when they encounter statistics. Most of the time, most people simply accept statistics without question.
Certainly, the article’s author didn’t ask many probing, critical questions about the C.D.F.'s claim. Impressed by the statistic, the author repeated it -- well, meant to repeat it. Instead, by rewording the C.D.F.'s claim, the author created a mutant statistic, one garbled almost beyond recognition.
But people treat mutant statistics just as they do other statistics -- that is, they usually accept even the most implausible claims without question. For example, the journal editor who accepted the author’s article for publication did not bother to consider the implications of child victims doubling each year. And people repeat bad statistics: The graduate student copied the garbled statistic and inserted it into the dissertation prospectus. Who knows whether still other readers were impressed by the author’s statistic and remembered it or repeated it? The article remains on the shelf in hundreds of libraries, available to anyone who needs a dramatic quote. The lesson should be clear: Bad statistics live on; they take on lives of their own.
Some statistics are born bad -- they aren’t much good from the start, because they are based on nothing more than guesses or dubious data. Other statistics mutate; they become bad after being mangled (as in the case of the author’s creative rewording). Either way, bad statistics are potentially important: They can be used to stir up public outrage or fear; they can distort our understanding of our world; and they can lead us to make poor policy choices.
The notion that we need to watch out for bad statistics isn’t new. We’ve all heard people say, “You can prove anything with statistics.” The title of my book, Damned Lies and Statistics, comes from a famous aphorism (usually attributed to Mark Twain or Benjamin Disraeli): “There are three kinds of lies: lies, damned lies, and statistics.” There is even a useful little book, still in print after more than 40 years, called How to Lie With Statistics.
Statistics, then, have a bad reputation. We suspect that statistics may be wrong, that people who use statistics may be “lying” -- trying to manipulate us by using numbers to somehow distort the truth. Yet, at the same time, we need statistics; we depend upon them to summarize and clarify the nature of our complex society. This is particularly true when we talk about social problems. Debates about social problems routinely raise questions that demand statistical answers: Is the problem widespread? How many people -- and which people -- does it affect? Is it getting worse? What does it cost society? What will it cost to deal with it? Convincing answers to such questions demand evidence, and that usually means numbers, measurements, statistics.
But can’t you prove anything with statistics? It depends on what “prove” means. If we want to know, say, how many children are “gunned down” each year, we can’t simply guess -- pluck a number from thin air: 100, 1,000, 10,000, 35 trillion, whatever. Obviously, there’s no reason to consider an arbitrary guess “proof” of anything. However, it might be possible for someone -- using records kept by police departments or hospital emergency rooms or coroners -- to keep track of children who have been shot; compiling careful, complete records might give us a fairly accurate idea of the number of gunned-down children. If that number seems accurate enough, we might consider it very strong evidence -- or proof.
The solution to the problem of bad statistics is not to ignore all statistics, or to assume that every number is false. Some statistics are bad, but others are pretty good, and we need statistics -- good statistics -- to talk sensibly about social problems. The solution, then, is not to give up on statistics, but to become better judges of the numbers we encounter. We need to think critically about statistics -- at least critically enough to suspect that the number of children gunned down hasn’t been doubling each year since 1950.
A few years ago, the mathematician John Allen Paulos wrote Innumeracy, a short, readable book about “mathematical illiteracy.” Too few people, he argued, are comfortable with basic mathematical principles, and this makes them poor judges of the numbers they encounter. No doubt this is one reason we have so many bad statistics. But there are other reasons, as well.
Social statistics describe society, but they are also products of our social arrangements. The people who bring social statistics to our attention have reasons for doing so; they inevitably want something, just as reporters and the other media figures who repeat and publicize statistics have their own goals. Statistics are tools, used for particular purposes. Thinking critically about statistics requires understanding their place in society.
While we may be more suspicious of statistics presented by people with whom we disagree -- people who favor different political parties or have different beliefs -- bad statistics are used to promote all sorts of causes. Bad statistics come from conservatives on the political right and liberals on the left, from wealthy corporations and powerful government agencies, and from advocates of the poor and the powerless.
In order to interpret statistics, we need more than a checklist of common errors. We need a general approach, an orientation, a mind-set that we can use to think about new statistics that we encounter. We ought to approach statistics thoughtfully. This can be hard to do, precisely because so many people in our society treat statistics as fetishes. We might call this the mind-set of the Awestruck -- the people who don’t think critically, who act as though statistics have magical powers. The awestruck know they don’t always understand the statistics they hear, but this doesn’t bother them. After all, who can expect to understand magical numbers? The reverential fatalism of the awestruck is not thoughtful -- it is a way of avoiding thought. We need a different approach.
One choice is to approach statistics critically. Being critical does not mean being negative or hostile -- it is not cynicism. The critical approach statistics thoughtfully; they avoid the extremes of both naive acceptance and cynical rejection of the numbers they encounter. Instead, the critical attempt to evaluate numbers, to distinguish between good statistics and bad statistics.
The critical understand that, while some social statistics may be pretty good, they are never perfect. Every statistic is a way of summarizing complex information into relatively simple numbers. Inevitably, some information, some of the complexity, is lost whenever we use statistics. The critical recognize that this is an inevitable limitation of statistics. Moreover, they realize that every statistic is the product of choices -- the choice between defining a category broadly or narrowly, the choice of one measurement over another, the choice of a sample. People choose definitions, measurements, and samples for all sorts of reasons: Perhaps they want to emphasize some aspect of a problem; perhaps it is easier or cheaper to gather data in a particular way -- many considerations can come into play. Every statistic is a compromise among choices. This means that every definition -- and every measurement and every sample -- probably has limitations and can be criticized.
Being critical means more than simply pointing to the flaws in a statistic. Again, every statistic has flaws. The issue is whether a particular statistic’s flaws are severe enough to damage its usefulness. Is the definition so broad that it encompasses too many false positives (or so narrow that it excludes too many false negatives)? How would
changing the definition alter the statistic? Similarly, how do the choices of measurements and samples affect the statistic? What would happen if different measures or samples were chosen? And how is the statistic used? Is it being interpreted appropriately, or has its meaning been mangled to create a mutant statistic? Are the comparisons that are being made appropriate, or are apples being confused with oranges? How do different choices produce the conflicting numbers found in stat wars? These are the sorts of questions the critical ask.
As a practical matter, it is virtually impossible for citizens in contemporary society to avoid statistics about social problems. Statistics arise in all sorts of ways, and in almost every case the people promoting statistics want to persuade us. Activists use statistics to convince us that social problems are serious and deserve our attention and concern. Charities use statistics to encourage donations. Politicians use statistics to persuade us that they understand society’s problems and that they deserve our support. The media use statistics to make their reporting more dramatic, more convincing, more compelling. Corporations use statistics to promote and improve their products. Researchers use statistics to document their findings and support their conclusions. Those with whom we agree use statistics to reassure us that we’re on the right side, while our opponents use statistics to try and convince us that we are wrong. Statistics are one of the standard types of evidence used by people in our society.
It is not possible simply to ignore statistics, to pretend they don’t exist. That sort of head-in-the-sand approach would be too costly. Without statistics, we limit our ability to think thoughtfully about our society; without statistics, we have no accurate ways of judging how big a problem may be, whether it is getting worse, or how well the policies designed to address that problem actually work. And awestruck or naive attitudes toward statistics are no better than ignoring statistics; statistics have no magical properties, and it is foolish to assume that all statistics are equally valid. Nor is a cynical approach the answer; statistics are too widespread and too useful to be automatically discounted.
It would be nice to have a checklist, a set of items we could consider in evaluating any statistic. The list might detail potential problems with definitions, measurements, sampling, mutation, and so on. These are, in fact, common sorts of flaws found in many statistics, but they should not be considered a formal, complete checklist. It is probably impossible to produce a complete list of statistical flaws -- no matter how long the list, there will be other possible problems that could affect statistics.
The goal is not to memorize a list, but to develop a thoughtful approach. Becoming critical about statistics requires being prepared to ask questions about numbers. When encountering a new statistic in, say, a news report, the critical try to assess it. What might be the sources for this number? How could one go about producing the figure? Who produced the number, and what interests might they have? What are the different ways key terms might have been defined, and which definitions have been chosen? How might the phenomena be measured, and which measurement choices have been made? What sort of sample was gathered, and how might that sample affect the result? Is the statistic being properly interpreted? Are comparisons being made, and if so, are the comparisons appropriate? Are there competing statistics? If so, what stakes do the opponents have in the issue, and how are those stakes likely to affect their use of statistics? And is it possible to figure out why the statistics seem to disagree, what the differences are in the ways the competing sides are using figures?
At first, this list of questions may seem overwhelming. How can an ordinary person -- someone who reads a statistic in a magazine article or hears it on a news broadcast -- determine the answers to such questions? Certainly news reports rarely give detailed information on the processes by which statistics are created. And few of us have time to drop everything and investigate the background of some new number we encounter. Being critical, it seems, involves an impossible amount of work.
In practice, however, the critical need not investigate the origin of every statistic. Rather, being critical means appreciating the inevitable limitations that affect all statistics, rather than being awestruck in the presence of numbers. It means not being too credulous, not accepting every statistic at face value. But it also means appreciating that statistics, while always imperfect, can be useful. Instead of automatically discounting every statistic, the critical reserve judgment. When confronted with an interesting number, they may try to learn more, to evaluate, to weigh the figure’s strengths and weaknesses.
Of course, this critical approach need not -- and should not -- be limited to statistics. It ought to apply to all the evidence we encounter when we scan a news report, or listen to a speech -- whenever we learn about social problems. Claims about social problems often feature dramatic, compelling examples; the critical might ask whether an example is likely to be a typical case or an extreme, exceptional instance. Claims about social problems often include quotations from different sources, and the critical might wonder why those sources have spoken and why they have been quoted: Do they have particular expertise? Do they stand to benefit if they influence others? Claims about social problems usually involve arguments about the problem’s causes and potential solutions. The critical might ask whether these arguments are convincing. Are they logical? Does the proposed solution seem feasible and appropriate? And so on. Being critical -- adopting a skeptical, analytical stance when confronted with claims -- is an approach that goes far beyond simply dealing with statistics.
Statistics are not magical. Nor are they always true -- or always false. Nor need they be incomprehensible. Adopting a critical approach offers an effective way of responding to the numbers we are sure to encounter. Being critical requires more thought, but failing to adopt a critical mind-set makes us powerless to evaluate what others tell us. When we fail to think critically, the statistics we hear might just as well be magical.
Joel Best is a professor of sociology and criminal justice at the University of Delaware. This essay is excerpted from Damned Lies and Statistics: Untangling Numbers From the Media, Politicians, and Activists, just published by the University of California Press and reprinted by permission. Copyright © 2001 by the Regents of the University of California.
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I agree that it’s way easier to trust the statistic than it is to think critically. The way invalid statistics are worded similarly to accurate statistics makes it hard to tell what is true or not true. Therefore, I agree that thinking critically is the one accurate way to fully understand the statistic than relying on how a statistic sounds.
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A few years ago NC State launched a critical thinking campaign to encourage this kind of thinking among students. It’s interesting to think about ways that we might foster critical thinking skills. And, perhaps some of the barriers to teaching these skills.
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I found this interesting because it really gave you a breakdown of what it would look like based on their statistic. I feel like it really shows how important it is to make sure that our statistics are accuarte in research, so that it is credible.
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I agree with this point and it’s also very surprising that an article would say such a thing when it is such a crazy statistic to state. Also, the fact that the student put that into his essay showed how people are so easy to believe crazy statistics like this if it is a “credible-looking” source.
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We’ll be talking a lot about credible sources next week. It’s an important step in creating our own research proposal. Hopefully, we will do a better job than the folks in this article did!
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Even if the researcher had not reviewed the original document to understand the unfortunate miswording. They should have deduced that the statement wasn’t possible. Had the student thought about the math behind the words, I think that he would have caught the error.
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This is an interesting example of how much people do not think twice when they hear or read a statistic. Joel Best (who I really enjoy and think has a great sense of humor) noted that the statistic seemed ridiculous the second he saw it, but the truth is most people wouldn’t blink at it.
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This is a great example of the more information, the better. When information is not clearly conveyed with direct quotes to the research or the context that it came from, it’s very easily manipulated. Not only that, but manipulation of a statistic within a larger work destroys credibility for the entire piece.
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This is such a great point Kelsey – context definitely matters and is so important. However, as we’ll talking more about in upcoming modules, journals and media often have word limits and other constraints that may make it difficult for researchers to include everything. In addition, the context often might not be "sexy’ enough for these publications to get the hits, likes, views, etc. that they want. It’s an interesting conundrum!
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I think that this sentence shows a very important aspect of research and how wording plays a huge role. Since the difference in the wording changed so much it showed a whole different meaning compared to the original sentence. In order to get the point across that the researcher is trying to convey, the wording needs to make sense and be accurate.
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Brianna, this is such a great point. We often spend a lot of time thinking about our research question, research design, and methods and forget that the words we use to share our findings have big consequences.
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The wording you decide to use when you are paraphrasing someone’s else words is so important because of this mistake. If your wording is incorrect then the whole statement is going to be wrong and you will be giving out false information.
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I think that this example teaches a very important lesson of how easily data can be twisted or skewed depending on the message which is trying to be conveyed. It also makes me think about how easy it is to believe a statistic, even if it is very incorrect.
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I think this is a very good point to make and unfortunately, is something that happens quite often. People will manipulate small things like wording and suddenly a small rain storm can become a typhoon.
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Yes and in addition to getting people more polarized and blowing things up, the data can have real consequences for people’s health, well-being, etc.
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Summarizing details from findings can be very tricky sometimes when we are trying not to plagairize. I feel like this happens a lot in today’s media. Many people over or under-exaggerate statistics they find.
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Sydney, your point about summarizing without plagiarizing is a strong one. Part of what I hope will happen in this class is that you’ll understand data better and the need to understand where it comes from (to go back to the original source, verify the data, etc.) so that we can better communicators about it.
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I think when it comes to looking into a statistic more it is important to start at the roots. Knowing how things have evolved and changed over time can give you a better look at what is going on in your data. Especially if one year there is a significant spike or decrease that will effect your data.
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This is why it’s important to understand the difference between rate and totals. The statistic is saying that the total number of child gun deaths doubled between those years, which is still a lot but the rate, like gun deaths/1000 people, is probably a more important number here.
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This is an excellent point. Sometimes when we are taking statistics and math classes we can wonder, “why are we learning this?” Guess what?! The point that you just made is exactly why.
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I think this is a very important comment because people oftentimes get confused in the media when “statistics” like this are published. These type of statistics that don’t account for changes, especially in population, can actually do more harm than good. It is important that statistics are reflective and representative of the current data INCLUDING changes.
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I think it is really important to realize that a statistic can mean something different person to person if it is not clearly stated. For example when I think of a child I think of infancy to eighteen years old but this article states that 25 is the age they consider to be the cap.
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Natalie – great point and next week in our Zoom workshop we are going to talk about how important it is to define exactly what we are talking about. We often assume that a lot of people know what we are talking about. Good researchers take their time and conceptualize and define everything – particularly things we take for granted and particularly messy social concepts. So glad you picked up on this so early!
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I completely agree with you Natalie. Statistics in general can be hard to understand sometimes so making sure the wording of the statement is clear is very important so that people can understand what they are reading.
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I have a question about this, so was the statistic stated by the C.D.F. not specific enough or was it only talking about children death by gun violence, it gets a little confusing here?
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I think that Joel Best (the author of this article) is critiquing the person who published that statistic by explaining that it can easily be misinterpreted leading to false beliefs, which could skew data analysis or public opinion.
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This is something that I am guilty of and working on. Often times, especially with social media, we hear so many “facts” and do not think to always check that it is actually a fact.
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This is exactly what I meant when I said most people read something and don’t think twice about it. The editor didn’t even think to question it.
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Most people accept statistics they see because they have no other knowledge that goes against the statistic. This can be very harmful and lead to false claims that may snowball into major issues.
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Yes, most people haven’t taken a social research methods course. And, this is a great illustration of how this course can help you in your life no matter what you study or do for a job.
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Bad statistics are incredibly common especially with how fast information can spread online. We end up getting lots of people who don’t have the knowledge to see that a statistic isn’t good or they misunderstand it. We saw this happen a lot during the height of the Covid pandemic.
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Dawn, good point. I got kinda burned out on talking about how much sociology there is in COVID but it is such a good example.
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I think this is very important to note on how there are so many fake statistics out there that continue to live on and create biased from those who encounter these statistics.
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In the upcoming modules, you’ll learn a little more about the pressure that researchers and publishers of journals often experience – pressure to have big headlines. For those of you who like to think about capitalism and the many critiques of it, you might find it interesting to think about how research, like pretty much everything else, is an industry where some people benefit.
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This is another great point! I totally agree that bad statistics are always repeated and are very harmful. The bad thing about these inaccurate statistics is that once they are published they are unable to be retracted. They are repeated by students, media outlets, professors, etc.
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I agree with the consensus that bad data is something that affects the future. It is something that affects future data as well as the opinions of others. Especially people who only do research once and believe that statistic to be true for the rest of their life never actually knowing that the data was false.
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I like the analogy the author used when describing statistics manipulated by the author’s wordings as ‘mutated’. It is important to remember that the words surrounding numerical data can easily alter the data’s meaning to support the author’s argument.
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Yes, and what we have to realize is that we are all human beings and have our own identities and biases. We can work to minimize the effects of our identities and biases as much as possible, but they are always there. What we need to do as researchers is be clear about what those are and how they influence the process AND to always be on the lookout for the influences in others’ research.
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I was thinking right along the same lines in regards to public fear. I think public fear creates lots of opportunity for “heros” to sweep in and skewed data sometimes does not seem to be accidental, but rather purposeful. This sentence just makes me think of intentions and having to dig deep before believing a dramatic statistic.
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It is important to note the lack of trust that people have in statistics when in the past statistics and numerical data were considered more trustworthy forms of data.
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I believe this point is something that we can learn from even now. As people don’t believe experts in their field may know more. Sometimes people will even believe other sources rather than someone who is more knowledgeable in a field because they believe they are untrustworthy. This lack of trust in our country leads to a lot of problems.
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This is such a struggle within our western culture. We continually struggle with needing the media and the statistics they give us, and not trusting the media and the accuracy of the statistics they give us.
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I don’t have much to say with this other than that I think it is very true and shows how important honest data collection is.
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Yes and these paragraph also reminded me of the interesting insights you all had in our workshop this week…when we were talking about how we need the media and headlines and yet the media and headlines also can cause problems.
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Statistics are important because they can tell us the truth about our reality but they can also be twisted in order to lie to us. For example, neo nazis will often cite the stat that black people only make 14% of the population but commit 50% of the crime because of an inherent characterstic of black people, but this is a misuse of the stat because in reality there are a bunch of factors that cause increased rate of crime by black people, such as the police acting as an occupying force in predominantly black neighborhoods, systemic poverty forcing people to commit crime to survive, a lack of social safety nets to help people when they are suffering, etc. All of the real factors that cause an increased rate of crime by black people are due to social factors and how our institutions operate, not something inherent to black people.
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I think this is important because it points out a methodical way where proof may be gathered to produce an accurate and informative statistic. The only issue here might be defining what is considered “gunned down”. Are children who are wounded by a bullet considered “gunned down” or are children who unfortunately passed away from bullet wounds considered “gunned down”?
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I think the author of this article is right. We should not give up on the numbers but be better. There are things we can do in a specific order in able to have better data and research outcomes.
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Del – what a great insight. All of this talk about what can go wrong can be really depressing and make us want to give up. But like we talked about in our workshop, there are troubling things that happen when we don’t use data and “check out.” I hope that we’ll all be inspired this semester to keep engaging in ways that are helpful.
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I think just like the activity we did in class we can’t just completely ignore the media because it gives us biased information as there are somethings that we have to listen to in order to be safe such as the weather etc. Similarly there are statistics that we could use in order to have important/real statistics that we could use in our own arguments, opinions or research.
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Love the connection you are making with what we discussed in class!
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I like how the author stated this and is saying that when we see something that doesn’t look quite right, we shouldn’t just say “oh well” and accept it but we should really think about it and then go do something about it.
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Riley, this is a great point and something that I am hoping we will all learn in this class – to dig deeper behind the information we are bombarded with!
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While I do agree to an extent that people often stick with what they are comfortable, if someone is never taught how to read a statistic properly or taught how to see if a statistic is unrealistic, then how is someone going to be able to separate things? That’s like handing a baby a shoe and assuming that since it can say shoe, it knows how to put a shoe on.
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Natalie – this is such a great point because it leads to the “now what” question. If people aren’t comfortable with basic mathematical principles then how do we either share information in a way that doesn’t require them to have these skills or how do we help them get these skills? This is one of the reasons that I love teaching methods because I feel like I am doing my part (even if it’s a very small one) in introducing folks to the importance of methods.
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This sentence made me think of our inquiry journals and how we look to understand why we are learning what we are and how we can understand fully. I think that these concepts relate to anytime we learn or want to learn. Understanding background and reason for needing the material allows for complete conclusions to be made.
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Context matters! We’ll talk about that more this semester. And great connection with the inquiry journals!
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Just like in my first comment I expressed how I agree with the author in believing that we must be better when encountering these different statistics. Having a plan and it being in order will help ensure better data for researchers’.
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How do you get rid of all your initial bias when it comes to working on creating a statistic. Especially in the beginning stages when it comes to research. I know there’s a lot of data that has a lot of bias making it correct in the sense of who it is about but not including everybody. So how do you not only eliminate bias but make sure to include everyone that you can?
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Sydney – this is a really thoughtful question! One of our next modules is about bias. I hope you’ll keep thinking about this question as you move through that module and our community-engaged project. I’ll be curious to hear more about what you and the rest of our class thinks.
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I also agree with Kathryn. Being critical is very important when dealing with data. Researchers need to be able to recognize the flaws as used in the article.
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Being critical is crucial when interpreting data. It is important to have healthy background knowledge on how to differentiate good and bad statistics, as well as knowing what is considered a ‘good’ or ‘bad’ statistic.
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I like to say that the social sciences teach us to approach everything,including data, with a healthy dose of skepticism.
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I, as well as many other people, often see random statistics posted on social media platforms. It is our choice whether or not we accept these numbers.
While sharing statistics can be very useful, it may backfire when people don’t like their meaning. This leads to hate comments arguing etc.
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Mia, this is a good point and part of why I am emphasizing asking questions so much in this course. We have to start asking questions if we are going to be able to uncover what underlying the data we encounter.
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I feel that this is one thing that is not talked about enough. We put our trust in these media sources and hope that they are reporting accurately, but there have been times when they have not and bigger problems arise.
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I think this is really important because oftentimes, especially at a younger age, we tend to take what we are told at face value. Not a lot of extra research goes into it. I like that he is saying to just not take everything at face value and don’t be afraid to dive in a little further.
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I really like this sentence because it stresses the importance of being educated, or willingness to learn.
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This portion is very important because it helps to show the credibility of Joel Best about the topic he is writing on. If someone is doing research, you would want to look at sources that are credible and big part is making sure the person who wrote the piece is a credible person.
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Great point Brianna! We’ll be talking a lot about credible sources next week as we start talking about our literature review.
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