For example, one popular video recommended injecting herbs into the prostate to treat cancer, which is unproven and potentially dangerous. Datasets are analyzed in ad hoc and exploratory ways. Engage with your friends and family on the problem of health misinformation. 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? Television is not the only media platform that can provide examples of bad statistics in the news. The graph generated a big controversy on social media, especially on Twitter, where users pointed out that the Georgia Health Department had repeatedly used misleading statistics during the COVID-19 outbreak. In critical scenarios such as a global pandemic, this becomes even more important as misinformation can lead to a higher spread and more deaths. Studies foster informed decision-making, sound judgments, and actions carried out on the weight of evidence, not assumptions. Remember, misuse of statistics can be accidental or purposeful. 2 Steven Strogatzs Twitter comment to show a recreation of a plot showing the number of daily cases of COVID-19 per 100,000 in the population of Kansas. Take this first example of a misleading graph that proves global warming is real. Verify the accuracy of information by checking with trustworthy and credible sources. A trailer video introducing the Community Toolkit that can be used for educational and training purposes. Such examples that appear in the purview of the general public have potential for motivating critical discourse around statistics content and interpretation that can lead to further curiosity of more advanced statistical thinking and reasoning. An infographic with tips on how to talk to your community about health misinformation. What information is missing from this data? We can all benefit from taking steps to improve the quality of health information we consume. As you saw throughout this post, illustrated with some insightful bad statistics examples, using data in a misleading way is very easy. When creating a graph to portray a statistic, it is natural to assume that the X and Y axes start at zero. When the Georgia Department of Public Health posted this plot (see Figure 3), it went viral because of what may have been intentional data manipulation. Insightful graphs and charts include a very basic, but essential, grouping of elements. Fact 1: The world's population is rapidly ageing. Statistics are nfi for to ability and capability to existing as misleading and bad data. This misleading data example is also referred to as data dredging (and related to flawed correlations). Absent these elements, visual data representations should be viewed with a grain of salt, taking into account the common data visualization mistakes one can make. Clearly, there is a correlation between the two, but is there causation? We will discuss this specific case in more detail later in the post. Data dredging is a self-serving technique often employed for the unethical purpose of circumventing traditional data mining techniques, in order to seek additional conclusions that do not exist. Papaya leaf juice, elderberry, dates, thyme, garlic, jasmine, limes, okra and other herbs, vegetables and exotic fruits were all offered this year as cures for cancer, diabetes, asthma and the flu.. As mentioned, this is not the only time Fox News has been criticized because of these situations. A first good thing would be, of course, to stand in front of an honest survey/experiment/research pick the one you have beneath your eyes , that has applied the correct techniques of collection and interpretation of data. Sometimes, it is better to just make a simple bar or even a table with a couple of columns so that something like this won't happen. Official websites use .govA .gov website belongs to an official government The issue comes with the second graph that is displayed in the article, in which we see a comparison of full-price sales between The Times and one of its biggest competitors, the Daily Telegraph. Omitting the baseline 5. Ioannidis JP. Really? But while that may be the case, people are duped by data visualizations every day. 73.6% of statistics are false. Annual Data 3. Lets take a look at some of the evidence for and against. You can see a graph that shows the UK National debt from 1995 to 2016. A 22-page overview of health misinformation and resources to stop it. For example, the objective graph literacy scale is a test with 13 items. 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. Surveys or studies conducted on a sample size audience often produce results that are so misleading that they are unusable. We all need access to trusted sources of information to stay safe and healthy. Ask a credible source, such as a doctor or nurse, if they have additional information. Over the next few paragraphs, we provide some possible ways of using the two previous cases to support learning of comparing samples and association, as well as how data visualizations can (mis)lead both unintentionally and intentionally if the consumer is not critically examining them. secure websites. Likewise, another common practice with data is omission, meaning that after looking at a large data set of answers, you only pick the ones that are supporting your views and findings and leave out those that contradict them. Rather its politicians trying to make a point for their own interest or just someone not understanding the information behind the graphs and charts they create, crime statistics are not free of being misleading. These studies are very soon contradicted by other important or outlandish findings. For example, if an urban planner sees that population growth in a certain part of the city is increasing at an exponential rate compared to other . Researchers should not allow their values, their bias, or their views to impact their research, analysis, or findings, therefore, looking at the way questions and findings are formulated is a good practice. However, the telling of half-truths through study is not only limited to mathematical amateurs. Expand efforts to build long-term resilience to misinformation, such as educational programs. For this last question, it would be important to make sure students are not merely concluding mask mandates lead to higher case rates than not having them. The novel coronavirus has forced the world to interact with data visualizations in order to make decisions at the individual level that have, sometimes, grave consequences. Understand the value of data types with this beginner's introduction! 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. If youre not sure, dont share. Intermediate data points should also be identified and context is given if it would add value to the information presented. This (mis)representation led to exaggerated claims about changes in cases, which was immediately evident when it was reported that Kansas counties that have mask mandates in place have seen a rapid drop in cases, while counties that only recommend their use have seen no decrease in cases, the states top health official said Wednesday (Hegeman Citation2020, August 5, emphasis added). For example, are visualizations representing the data accurately? 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. Grueskin shared some of these insightful examples of misleading statistics in the news in a Twitter thread that became very popular. Reuters / Via reddit.com 2. This is an absolute reduction of 1.2% over 4 years, or 0.3% annually. After showing this plot to students, some useful questions could be: Fig. I have mentioned the most common mistakes that can lead to misleading or misuse of statistics. Incentivize coordination across grantees to maximize reach, avoid duplication, and bring together a diversity of expertise. The selective bias is slightly more discreet for those who do not read the small lines. The above graph/chart was presented as a point of emphasis. This is problematic because this plot was used to describe statistical trends directly to the general public. Look at the About Us page on the website to see if you can trust the source. No matter how good a study might be, if it's not written using objective and formal language, then it is at risk to mislead. Accepted author version posted online: 12 Apr 2021, Register to receive personalised research and resources by email. Survival Rates in Cancer Survival rates are often used as a measure of cancer treatment success. For these reasons, a firm understanding of data science is an essential skill for professionals. The first example of misleading data visualization comes to us courtesy of Reddit but was originally propagated by Fox news. In this case 100/1.2% =88. It would be preposterous to say that they cause each other and that is exactly why it is our example. Carefully review information in preprints. For example, let's say you're comparing mammal weights. The next of our most common examples for misuse of statistics and misleading data is, perhaps, the most serious. Truncating axes means doing the opposite. What Is A Misleading Statistic? By Bernardita Calzon in Data Analysis, Jan 6th 2023, 3) Misleading Statistics Examples In Real Life. 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. To get this journey started, let's look at a misleading statistics definition. 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. An official website of the For the presidential run of 2012, the news network showed the graph below where we see a pie chart displaying a total of 193% which is clearly wrong and misleading as the total should be 100%. Omitting the baseline 5. As mentioned at the beginning of this article, it has been shown that a third of the scientists admitted that they had questionable research practices, including withholding analytical details and modifying results! The plot compared the number of COVID-19 cases over time for counties in Kansas that had mask mandates versus those that did not. 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. Nutrition studies have a particularly bad reputation in the news. A good rule of thumb is to always take polling with a grain of salt and to try to review the questions that were actually presented. (Citation2012) titled Case Studies for Quantitative Reasoning: A Casebook of Media Articles. Improper bubble sizes 13. Purposeful bias is the deliberate attempt to influence findings without even feigning professional accountability. Secure .gov websites use HTTPSA lock ( The plot that was originally posted to the Georgia Department of Public Health website (image provided by Twitter user Calling Bullshit, Figure 3) appears to show that the number of COVID-19 cases in the top five counties in the state, at the time, were consistently dropping over the previous month. Consider the following steps to determine if information is accurate: For more information on common types of health misinformation sources, check out our Health Misinformation Community Toolkit. 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. Partner with community groups and other local organizations to prevent and address health misinformation. The misuse of statistics is a much broader problem that now permeates multiple industries and fields of study. Learn how to identify and avoid sharing health misinformation. Statistics are infamous for their ability and potential to exist as misleading and bad data. The case started when the giant pharmaceutical company, Purdue Pharma, launched its new product OxyContin, which they advertised as a safe, non-addictive opioid that was highly effective for pain relief. 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. Accurate vaccine information is critical and can help stop common myths and rumors. Based on the structure of the chart, it does in fact appear to show that the number of abortions since 2006 experienced substantial growth, while the number of cancer screenings substantially decreased. While certain topics listed here are likely to stir emotion depending on ones point of view, their inclusion is for data demonstration purposes only. Example #1. To make sure the reliability is high, there are various techniques to perform the first of them being the control tests, which should have similar results when reproducing an experiment in similar conditions. It also happens to be a topic that is vigorously endorsed by both opponents and proponents via studies. Statistics presented without context should be viewed critically. Here are some more examples of missed opportunities to do so. Use this checklist everytime you come across health-related content you are not sure about. It is worth mentioning that 1998 was one of the hottest years on record due to an abnormally strong El Nio wind current. . It is a data mining technique where extremely large volumes of data are analyzed for the purpose of discovering relationships between different points. For some effective examples of visual information, check out this visualization of wealth shown to scale, or Nicky Case's website, which is full of interactive games that explain how society works. Yet, as we learned from the Argentinian graph, looks can deceive. While it is quite clear that statistical data has the potential to be misused, it can also ethically drive market value in the digital world. Columbia Journalism School professor Bill Grueskin even made a lesson to its students about the topic and used several misleading charts from the US news show as an example of what not to do when presenting data. ) or https:// means youve safely connected . You can see the updated version below. Finally, how big was the sample set, and who was part of it? Scientists! Using the pair of graphs in the first case, a question that could spur thinking about these two phenomenacounties with vs without a mask mandatecould be something like: What does this graph (Figure 1, the one with two axes) make it appear is happening? Bias is most likely to take the form of data omissions or adjustments to prove a specific point. 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. Oftentimes, data fishing results in studies that are highly publicized due to their important or outlandish findings. 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 ). Did we forget to mention the amount of sugar put in the tea or the fact that baldness and old age are related just like cardiovascular disease risks and old age? So, let's explore some interesting choices of using data visualization tools and discuss why they are misleading. In 2012, the global mean temperature was measured at 58.2 degrees. Likewise, in order to ensure you keep a certain distance to the studies and surveys you read, remember the questions to ask yourself - who researched and why, who paid for it, and what was the sample. As a result, the lack of statistical literacy among the general public, as well as organizations that have a responsibility to share accurate, clear, and timely information with the general public, has resulted in widespread (mis)representations and (mis)interpretations. Although the hope would be that students recognize the misleading horizontal axis, it is important to point attention directly to it so that students begin to learn to dissect such visualizations by being critical of scalinga common point of intentional or unintentional misrepresentation of dataas they work toward becoming critical consumers. Why most published research findings are false. Going https://rigorousthemes.com/blog/misleading-data-visualization-examples/ Category: Health Show Health An official website of the United States government. This slide includes the key takeaways from the advisory. In this case, the goal is not association, but comparison, thereby making it a bit more difficult to initially interpret the data. Uncover the power of spider charts with this complete guide including examples, best practices, and more! In a similar fashion, once students have begun to develop an understanding of associationa topic beginning in the eighth grade under CCSSM, and appearing in tertiary statistics as well as quantitative reasoning coursesa time-series plot might be shared, such as the one in Figure 4 taken from this blog post (Acquah Citation2020, May). xkdc's comic illustrates this very well, to show how the "fastest-growing" claim is a totally relative marketing speech: Likewise, the needed sample size is influenced by the kind of question you ask, the statistical significance you need (clinical study vs business study), and the statistical technique. The graph shows the growth of COVID-19 cases from March 5 to March 31. to the .gov website. Yet, closer examination will reveal that the chart has no defined y-axis. Cherry Picking 2. Confusing statistics refers to the improper of numberic data moreover intentionally or by . The below chart expresses the 30-year change in global mean temperatures. Address health misinformation in your community. Purposely or not, the time periods we choose to portray will affect the way viewers perceive the data. 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. Although this controversy happened around 1996, the case of Purdue Pharma and their highly addictive drug OxyContin is still affecting thousands of American citizens and has already taken the lives of thousands of others to this date, all due to the misuse of statistics as a marketing tactic. The example above is an example of selective bias; the biologists were recruited, not randomly selected. Want to test a professional data analysis software? For instance, showing a value for 3 months can show radically different trends than showing it over a year. 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. Institute of Medicine (US) Committee on Quality of Health Care in America. This is reported by the makers of Fosamax accurately as a 56% reduction in risk, which is true but misleading. That marked the highest percentage since at least 1968, the earliest year for which the CDC has online records. If you still want to use the data to make a point, you can make sure to mention the small sample size as a disclaimer. Fig. Another common misuse of statistics is strategically picking the time period to show a result. For example, while France had almost 150 years to adapt to a change . The growing number of places people go to for information has made it easier for misinformation to spread at a never-before-seen speed and scale. Disinformation is when misinformation is used to serve a malicious purpose, such as to trick people into believing something for financial gain or political advantage. This misleading tactic is frequently used to make one group look better than another. There are two take-aways when comparing the two plots. Misleading statistics refers to the misuse of numerical data either intentionally or by error. Examples of misuse of statistics in the media are very common. U.S. Department of Health and Human Services. However, when considering other factors such as the health conditions in which patients arrived at the hospitals we can drive other conclusions. Give researchers access to useful data to properly analyze the spread and impact of misinformation. Knowing when data is accurate and complete, and being able to identify discrepancies between numbers and any . 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. There, they speak about two use cases in which COVID-19 information was used in a misleading way.