Scientists! These controlling measures are essential and should be part of any experiment or survey unfortunately, that isnt always the case. Train journalists, editors, and other media professionals to recognize, correct, and avoid amplifying misinformation. Furthermore, an essential discussion should center around why specific locations may have had a mask mandate versus why others may not have, and to focus attention on the change over time within each grouprather than comparing between the groups. This is not to say that there is no proper use of data mining, as it can in fact lead to surprise outliers and interesting analyses. Here's my top five falsehoods-in-figures: 1. The available information and expert opinion seems to vacillateone year fats are terrible for you and the next they are a health food. At a glance, the chart makes you believe that The Times has twice as many full-price subscriptions as its competitor. On August 6, Steven Strogratz posted the following plot on Twitter (see Figure 2), which was a recreation of the plot produced by the Kansas Department of Health and Environment with the right side vertical scale removed and both categories of data appropriately placed on the same scale. Together, we have the power to build a healthier information environment. Managing Partners: Martin Blumenau, Ruth Pauline Wachter | Trade Register: Berlin-Charlottenburg HRB 144962 B | Tax Identification Number: DE 28 552 2148, News, Insights and Advice for Getting your Data in Shape, BI Blog | Data Visualization & Analytics Blog | datapine, NASAs Goddard Institute for Space Studies. . A trailer video introducing the Community Toolkit that can be used for educational and training purposes. For example, one popular video recommended injecting herbs into the prostate to treat cancer, which is unproven and potentially dangerous. Truncating axes means doing the opposite. The number of people aged 60 years or older will rise from 900 million to 2 billion between 2015 and 2050 (moving from 12% to 22% of the total global population). Why might the COVID-19 case rates be higher in counties with mask mandates than those without? From there naturally stems the question: who paid them? This presents opportunities for statistics educators and statistics teacher educators to reflect on these (mis)representations and leverage them as teaching and learning opportunities to build statistical literacy. The prevalence of health misinformation was the highest on Twitter and on issues related to smoking products and drugs. Cherry picking data. It further appears to indicate that counties with no mask mandate have seen relatively no change in number of daily cases. The example above is an example of selective bias; the biologists were recruited, not randomly selected. . They're infallible, concrete, impossible to argue with -- however you want to spin it, they make one solid point. In 2007, Colgate was ordered by the Advertising Standards Authority (ASA) of the U.K. to abandon their claim: More than 80% of Dentists recommend Colgate. The slogan in question was positioned on an advertising billboard in the U.K. and was deemed to be in breach of U.K. advertising rules. This list of misleading statistics fallacy examples would not be complete without referencing the COVID-19 pandemic. Lets explain this better with an example. We note that these examples come from the context of the United States as that is the context the authors are most familiar with, however, from scanning the news, these seem to be issues common across the world during this highly politicized global pandemic where peoples lives and politicians power are in danger. Take care to apply data responsibly, ethically, and visually, and watch your transparent corporate identity grow. 19 Most Misleading Statistics (That Are Technically Correct) By: Cracked Plasticians April 20, 2016 Advertisement When the math adds up, the numbers never lie. You can also ask someone external to your research to look at the data, someone biased to the topic that can confirm your results are not misleading. The Surgeon Generals Community Toolkit for Addressing Health Misinformation provides specific guidance and resources for health care providers, educators, librarians, faith leaders, and trusted community members to understand, identify, and stop the spread of health misinformation in their communities. For instance, the nature of the group of people surveyed: asking a class of college students about the legal drinking age, or a group of retired people about the elderly care system. Going against convention 8. However, a closer look shows that the X-axis starts at 420,000 instead of 0. In CCSSM, students gain experiences with histograms beginning in grade 6, and they begin comparing multiple plots as early as the seventh grade. Next, in our list of bad statistics examples, we have the case of a popular toothpaste brand. Example 8: Urban Planning. This example of a misleading use of statistics is perhaps one of the more clear cases of intent to mislead, despite attempts of the administration to make it appear accidentalsee May 19 story about the response in The Atlanta Journal-Constitution (Mariano and Trubey 2020 ). Businesses and analysts are exposed to making biases when a single person is doing an entire analysis. This is paired with the fact that counties are not always depicted in the same order, but instead in descending order of cases. There are two take-aways when comparing the two plots. This is a case of misleading statistics that can be done purposely, to achieve a specific result, or accidentally. Another issue, and maybe the worst of them all, is that the dates under the bars are not ordered chronologically. There are different ways in which statistics can be misleading that we will detail later. This graph makes the argument that masks help "flatten the curve" (or lower the rate of growth of COVID-19 cases) by pointing out that countries with mask usage had lower growth rates than countries without mask usage. A study of millions of journal articles shows that their authors are increasingly reporting p-values but are often doing so in a misleading way, according to a study by researchers at the Stanford University School of Medicine.P-values are a measure of statistical significance intended to inform scientific conclusions. Here is a guide from the CDC on the myths and facts about COVID-19 vaccination. You can be drawn in by the good from what appears to be a reputable source and then can. Consider headlines and images that inform rather than shock or provoke. We can all benefit from taking steps to improve the quality of health information we consume. These two questions are likely to provoke far different responses, even though they deal with the same topic of government assistance. 1. In the digital age, these capabilities are only further enhanced and harnessed through the implementation of advanced technology and business intelligence software. The Govenor race where one guy's 37% was WAY more than just 37% gravismarketing.com / Via reddit.com 4. Fig. Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine. In this article, we showcase examples of how data related to the COVID-19 pandemic has been (mis)represented in the media and by governmental agencies and discuss plausible reasons why it has been (mis)represented. As we can see, the X axes here start from 590 instead of zero. The Cake Is a Lie. Insightful graphs and charts include a very basic, but essential, grouping of elements. For instance, of 100 patients that arrived in poor condition in Hospital A, 30 survived. Asking a question to a sample size of 20 people, where 19 answers "yes" (=95% say for yes) versus asking the same question to 1,000 people and 950 answers "yes" (=95% as well): the validity of the percentage is clearly not the same. It would be preposterous to say that they cause each other and that is exactly why it is our example. Really? And over the years, tobacco. Advanced technology solutions like online reporting software can enhance statistical data models, and provide digital age businesses with a step up on their competition. This is problematic because this plot was used to describe statistical trends directly to the general public. For example, let's say you're comparing mammal weights. In an undergraduate-level context, it is fairly common to reason about side-by-side histograms, or to create them, in statistics courses or quantitative reasoning courses. A more helpful way to look at this is the NNT (Number needed to treat, defined in statistics using the formula 100/%reduction). Providing solely the percentage of change without the total numbers or sample size will be totally misleading. The plot compared the number of COVID-19 cases over time for counties in Kansas that had mask mandates versus those that did not. The next of our most common examples for misuse of statistics and misleading data is, perhaps, the most serious. Establish quality metrics to assess progress in information literacy. To the question "can statistics be manipulated? The source of the initial criticism appears to have come from The Rachel Maddow Show (yes, the same one that shared a poorly crafted data visualization in Case 1, but carefully dissected the (mis)representation in this case), which can be viewed in a short video tweeted on May 15 by Acyn Torabi. We all need access to trusted sources of information to stay safe and healthy. Recently, Kellogg's UK was hit with a ban from the ASA (Advertising Standards Authority) after making false health claims in its advert for Special K cereal. Each of these sources may have other primary purposes, so there are advantages and challenges when they are used for the purposes of quality measurement and reporting. It is, therefore, argued by global warming opponents that, as there was a 0.1-degree decrease in the global mean temperature over a 14-year period, global warming is disproved. This plot (Figure 2) shows something quite different than the one shared by the Kansas Department of Health and Environment in the August 5 press conference. The claim, which was based on surveys of dentists and hygienists carried out by the manufacturer, was found to be misrepresentative as it allowed the participants to select one or more toothpaste brands. Was there a rapid decline in cases? Overloading readers with data 9. It is generally agreed upon that the global mean temperature in 1998 was 58.3 degrees Fahrenheit. We then build on these examples to draw connections to how they could be used to enhance statistics teaching and learning, especially as it relates to secondary and introductory tertiary statistics coursework. Example #1. About eight-in-ten U.S. murders in 2021 - 20,958 out of 26,031, or 81% - involved a firearm. 3099067 It is also worth noting that, as there is a large degree of variability within the climate system, temperatures are typically measured with at least a 30-year cycle. Here are common types of misuse of statistics: Now that you know them, it will be easier to spot them and question all the stats that are given to you every day. With the increasing reliance on intelligent solution automation for variable data point comparisons, best practices (i.e., design and scaling) should be implemented prior to comparing data from different sources, datasets, times, and locations. As no one works for free, it is always interesting to know who sponsors the research. - Do you think that the government should help those people who cannot find work? Bias is most likely to take the form of data omissions or adjustments to prove a specific point. 19 of the persons respond yes to the survey. No, of course, its a made-up number (even though such a study would be interesting to know but again, could have all the flaws it tries at the same time to point out). Misinformation is information that is false, inaccurate, or misleading according to the best available evidence at the time. The time an upside down y-axis made "Stand Your Ground" seem much more reasonable. Spain and Italy have large populations, but enormous. Increase investment in research on misinformation. In the field of healthcare, statistics is important for the following reasons: Reason 1: Statistics allows healthcare professionals to monitor the health of individuals using descriptive statistics.. Reason 2: Statistics allows healthcare professionals to quantify the relationship between . Cherry Picking 2. tristin mays big time rush; During the initial stages of COVID, the general public was forced to consume scientific information in the form of data visualizations to stay informed about the current developments of the virus. You can be the judge. It also happens to be a topic that is vigorously endorsed by both opponents and proponents via studies. Oftentimes, data fishing results in studies that are highly publicized due to their important or outlandish findings. False or misleading information is causing people to make decisions that could have dangerous consequences for their health. Incentivize coordination across grantees to maximize reach, avoid duplication, and bring together a diversity of expertise. Misleading graphs are a source of misinformation that worry many experts. As such, this is a great misleading statistics example, and some could argue bias considering that the chart originated not from the Congressman, but from Americans United for Life, an anti-abortion group. As you saw throughout this post, illustrated with some insightful bad statistics examples, using data in a misleading way is very easy. The name and date of birth used in this example are imaginative, used for illustrative purposes, and do not represent an actual patient. Home Uncategorized examples of misleading statistics in healthcare. Many would falsely assume, yes, solely based on the strength of the correlation. Assess the impact of health misinformation. Lets put this into perspective with an example of the misuse of statistics in advertising. Data (Mis)representation and COVID-19: L . 2 Cases of COVID Data Being (Mis)represented, https://doi.org/10.1080/26939169.2021.1915215, https://www.causalflows.com/introduction/, https://www.amstat.org/asa/education/Guidelines-for-Assessment-and-Instruction-in-Statistics-Education-Reports.aspx, http://www.thefunctionalart.com/2020/05/about-that-weird-georgia-chart.html, https://www.statisticsteacher.org/2019/09/19/using-locus-released-items/, https://apnews.com/f218e1a38cce6b2af63c1cd23f1d234e, https://twitter.com/MaddowBlog/status/1291553722527604736?s=20, https://www.ajc.com/news/stateregional-govtpolitics/just-cuckoo-state-latest-data-mishap-causes-critics-cry-foul/182PpUvUX9XEF8vO11NVGO/, http://www.stat.auckland.ac.nz/iase/serj/SERJ5(2).pdf#page=30. This is with the same aim of making it seem like the cases are dropping. Much like abortion, global warming is another politically charged topic that is likely to arouse emotions. Citation2020), this very truth has now been laid bare for the world to see in the media and social media as the general public grapples with making, and making sense of, data-based arguments around COVID-19. Learn how to identify and avoid sharing health misinformation. Partner with community groups and other local organizations to prevent and address health misinformation. Ask a credible source, such as a doctor or nurse, if they have additional information. There are plenty of examples available, but looking to the world of hockey, a team that gets the puck in their opponents' end during a power play and just cycle for two minutes without taking a shot actually waste a two minute opportunity with puck possession. In this case, the goal is not association, but comparison, thereby making it a bit more difficult to initially interpret the data. After a discussion, and a conclusion that attempts to make a generalized claim beyond the data (i.e., an inference; also a seventh-grade standard), the adjusted plot (Figure 2) could be shared with questions such as: Does the conclusion still hold when the plot is adjusted to accurately depict the two situations? Clearly, there is a correlation between the two, but is there causation? Instead, we see the dates between April and May interspersed with the aim of making viewers of this graph believe that the cases are gradually decreasing. It becomes hard to believe any analysis! So, can statistics be manipulated? Big data has the ability to provide digital age businesses with a roadmap for efficiency and transparency, and eventually, profitability. Under the CCSSM, beginning in the seventh grade, students are expected make comparisons between different samples on the same attribute. Our guide included some misleading examples and illustrations of data, several of which come from the Reddit thread for misleading visual statistics. And finally, if youre not sure about the content dont share it. However, when you look at a longer time period such as 1910 to 2015 (image below) we realize that the debt is actually very low comparing it to other years. A quick look shows that counties with mask mandates (the orange line) in place have shown a stark decline in COVID-19 cases over the course of about 3 weeks that has led to lower case numbers than counties without a mask mandate. Lets take a look at some of the evidence for and against. When Research Evidence is Misleading. Grueskin shared some of these insightful examples of misleading statistics in the news in a Twitter thread that became very popular. On top of that, the numbers can be hard to interpret, whether that's a . Revisit this insightful list of bad statistics examples from time to time to remind you of the importance of using data in a proper way! Remember, misuse of statistics can be accidental or purposeful. By Dana Litt and Scott Walters, March 24, 2021. It demonstrates the change in air temperature (Celsius) from 1998 to 2012. On the other side, of 400 patients that arrived in poor condition at Hospital B, 210 survived at a survival rate of 52.5%. As businesses are often forced to follow a difficult-to-interpret market roadmap, statistical methods can help with the planning that is necessary to navigate a landscape filled with potholes, pitfalls, and hostile competition. How inclusive was it? Now, if the issue here is not obvious enough, we can see that the Y-axis in this chart starts from 58% and ends at 78%, making the 12% drop from 2009 to 2019 look way more significant than it actually is. Now that weve put the misuse of statistics in context, lets look at various digital age examples of statistics that are misleading across five distinct, but related, spectrums: media and politics, news, advertising, science, and healthcare. Datasets are analyzed in ad hoc and exploratory ways. This page includes the key takeaways from the advisory. Health misinformation has also led to harassment and violence against health workers, airline staff, and other frontline workers tasked with communicating evolving public health measures. The problem with correlations is this: if you measure enough variables, eventually it will appear that some of them correlate. But, what about causation? Use this checklist everytime you come across health-related content you are not sure about. The most recent case happened not too long ago in September 2021. Manipulating the Y-axis+ 6. Number don't add up 11. The power of words is huge, therefore, carefully looking at the way a study is written is another great practice to assess its quality. The below chart expresses the 30-year change in global mean temperatures. Whether this person notices or not, they might be providing an inaccurate or manipulated picture to confirm a specific conclusion. Nutrition studies have a particularly bad reputation in the news. Secure .gov websites use HTTPSA lock ( Annual Data 3. Politifact, a fact-checking advocacy website, reviewed Rep. Chaffetzs numbers via a comparison with Planned Parenthoods own annual reports. To avoid this issue, you should always pick a random sample of people whose background may or may not be related to the topic of the survey. Carefully review information in preprints. Register a free Taylor & Francis Online account today to boost your research and gain these benefits: Data (Mis)representation and COVID-19: Leveraging Misleading Data Visualizations For Developing Statistical Literacy Across Grades 616, a School of Teacher Preparation, Administration & Leadership, New Mexico State University, Las Cruces, NM, b Department of Curriculum and Instruction, University of Houston, Houston, TX, GAISE College Report ASA Revision Committee. Not using annotations 12. To illustrate, a survey asks 20 people a yes-or-no question. Small samples underrepresent your target audience. similar name packaging or misleading presentations of drug strength or dosage, . Quasi-experimental, single-center, before and after studies are enthusiastically performed. Now, the obvious answer is going for option A. Misuse of statistics often happens in advertisements, politics, news, media, and others. Figure 1, from the Healthgrades site, shows the results for the first. This a sad representation of how dangerous misinformation can be. Verify the accuracy of information by checking with trustworthy and credible sources. The birth rate for . However, at the time this graph was published, many media publications interpreted the graph as if the deaths dropped, showing how damaging the misuse of graphs and numbers can be. American network Fox News has been under scrutiny several times throughout the years for showing misleading statistics graphs that seem to purposely portray a conclusion that is not accurate. Furthermore, those without the statistical literacy to recognize it, many times, are further convinced that statistics is not a reliable or trustworthy source of evidence. For further thinking about this topic, I recommend this blogpost (Rost Citation2018, May). please save N95s and surgical masks for our healthcare workers who . You can see a graph that shows the UK National debt from 1995 to 2016. That marked the highest percentage since at least 1968, the earliest year for which the CDC has online records. The above graph/chart was presented as a point of emphasis. These are important questions to ponder and answer before spreading everywhere skewed or biased results even though it happens all the time, because of amplification.
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