examples of misleading statistics in healthcare30 Apr examples of misleading statistics in healthcare
By Bernardita Calzon in Data Analysis, Jan 6th 2023, 3) Misleading Statistics Examples In Real Life. For example, are visualizations representing the data accurately? This video can be used for educational and training purposes. This misleading tactic is frequently used to make one group look better than another. Survival Rates in Cancer Survival rates are often used as a measure of cancer treatment success. This list of misleading statistics fallacy examples would not be complete without referencing the COVID-19 pandemic. The time an upside down y-axis made "Stand Your Ground" seem much more reasonable. There is also no evidence to say that the Florida Law Enforcement Department was purposely deceiving the public. Examples of misuse of statistics in the media are very common. 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. Misleading pie chart 4. 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. 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. It is easy to see a correlation. Broad dissemination and consumption of false or misleading health information, amplified by the internet, poses risks to public health and problems for both the health care enterprise and the government. 1) Misleading Data Visualization Examples 2) How to Avoid Misleading Visuals 3) The Impact Of Bad Data Visualizations Nobody likes feeling manipulated in any way, shape, or form. Ignoring the uncertainty of the collected data or numbers. Engage with your friends and family on the problem of health misinformation. For example, if you have a data set with a diastolic blood pressure range of 230 (highest diastolic value) to 25 (lowest diastolic value) = 205 (range), an error probably exists in your data because the values of 230 and 25 aren't valid blood pressure measures in most studies. We will discuss this specific case in more detail later in the post. PLoS Med. In this case, it can create the wrong idea of a product being healthier than it actually is. Each kind is calculated differently and gives different information (and a different impression) about the data: Manipulating the Y-axis+ 6. Home Uncategorized examples of misleading statistics in healthcare. Really? Some misleading online posts are difficult to spot because they contain both good and bad medical advice. Address health misinformation in your community by working with schools, community groups, and health care professionals to develop local strategies against misinformation. We start by showing a less obvious example of how having statistical literacy, including an understanding of the art of data visualization, can cause speculation about (mis)representations of data. Truncating axes is a very dangerous false statistics practice, as it can help create wrong narratives around important topics. The most recent case happened not too long ago in September 2021. More than half of all suicides in 2021 - 26,328 out of 48,183, or 55% - also involved a gun, the highest percentage since 2001. Continue to modernize public health communications. Businesses and analysts are exposed to making biases when a single person is doing an entire analysis. Omitting baselines, or the axis of a graph, is one of the most common ways data is manipulated in graphs. Amongst various videos of success cases of patients, merchandising, and unethical messaging included in Purdues marketing strategy to advertise OxyContin as a safe drug, there was a very interesting graph, used to prove to doctors that the drug was non-addictive because it stayed on the patients blood over time avoiding symptoms of withdrawal. Official websites use .govA .gov website belongs to an official government Using the wrong graph 7. Verify the accuracy of information by checking with trustworthy and credible sources. For further thinking about this topic, I recommend this blogpost (Rost Citation2018, May). For example, on a poll seeking tax opinions, lets look at the two potential questions: - Do you believe that you should be taxed so other citizens dont have to work? 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. 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. Amplify communications from trusted messengers and subject matter experts. Going https://rigorousthemes.com/blog/misleading-data-visualization-examples/ Category: Health Show Health Second, without paying very close attention to the scales of the two vertical axes in the original plot, it would be easy to conclude that counties with mask mandates had dropped below that of those with no mask mandatean incorrect conclusion. As individuals, we can help stop the spread of misinformation by taking the following steps: Everyone has the power to stop misinformation from spreading. Many would falsely assume, yes, solely based on the strength of the correlation. Each is likely a result of a third factor, that being: an increased population, due to the high tourism season in the month of June. For example, during the COVID-19 pandemic misinformation has caused people to decline COVID-19 vaccines, reject public health measures such as masking and physical distancing, and use unproven treatments. Depending on the measure, data can be collected from different sources, including medical records, patient surveys, and administrative databases used to pay bills or to manage care. Datasets are analyzed in ad hoc and exploratory ways. 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. Now, if we take a closer look at this chart we can find a few mistakes that make the information very misleading. As you saw throughout this post, illustrated with some insightful bad statistics examples, using data in a misleading way is very easy. 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? Health Misinformation Current Priorities of the U.S. Health (2 days ago) Office of the U.S. A controversial representation of this happened in 2014 when a graph depicting the number of murders committed using firearms in Florida from 1990 to 2010 was published in the context of the Stand Your Ground law, enacted in 2005 to give people the right to use deadly force for self-defense. 19 of the persons respond yes to the survey. 5 Howick Place | London | SW1P 1WG. And now have a look at the trend from 1900 to 2012: While the long-term data may appear to reflect a plateau, it clearly paints a picture of gradual warming. In critical scenarios such as a global pandemic, this becomes even more important as misinformation can lead to a higher spread and more deaths. 2005;2 (8):e124. 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. As one out of twenty will inevitably be deemed significant without any direct correlation, studies can be manipulated (with enough data) to prove a correlation that does not exist or that is not significant enough to prove causation. The misuse of statistics is a much broader problem that now permeates multiple industries and fields of study. Each year, millions of research hypotheses are tested. Misleading Coronavirus graphs. These two questions are likely to provoke far different responses, even though they deal with the same topic of government assistance. Several Twitter users began attempting to make sense of what the data were actually saying. At a first glance, the graph, which is displayed below, shows a descending trend that starts the year the law was enacted, concluding that Stand Your Grown is responsible for the apparent drop in the number of murders committed using firearms in the years after it was implemented. 3099067 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. Lets put this into perspective with an example of the misuse of statistics in advertising. Yet, as we learned from the Argentinian graph, looks can deceive. (labels are clear, axes begin at 0, right chart type, etc) Is the research represented honestly and in an impartial manner? Improper bubble sizes 13. Mixing up linear and logarithmic scales. We apologize. On the other side, of 400 patients that arrived in poor condition at Hospital B, 210 survived at a survival rate of 52.5%. 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. These false correlations often leave the general public very confused and searching for answers regarding the significance of causation and correlation. However, the telling of half-truths through study is not only limited to mathematical amateurs. Use a broader range of credible sourcesparticularly local sources. As no one works for free, it is always interesting to know who sponsors the research. Number don't add up 11. 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. Cumulative VS. 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. When Research Evidence is Misleading. It is a data mining technique where extremely large volumes of data are analyzed for the purpose of discovering relationships between different points. The image below is a great example of this misleading practice. In May 2020, around 5 months after COVID-19 started spreading around the world, the US Georgia Department of Public Health posted a chart that aimed at showing the top 5 counties that had the highest COVID-19 cases in the past 15 days and the number of cases over time. Here are some more examples of missed opportunities to do so. 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. Here's my top five falsehoods-in-figures: 1. Proactively address the publics questions. While a malicious intent to blur lines with misleading statistics will surely magnify bias, the intent is not necessary to create misunderstandings. 73.6% of statistics are false. (1 days ago) WebMisleading Data Visualization Examples 1. newrepublic.com / Via reddit.com Advertisement 3. But this didnt come easy. U.S. Department of Health and Human Services, Reasons to use the Community Toolkit video, Talk to your community about health misinformation, Share Myths and facts about COVID-19 vaccines to Facebook, Share Myths and facts about COVID-19 vaccines to Twitter, Share Myths and facts about COVID-19 vaccines on LinkedIn, Share Myths and facts about COVID-19 vaccines in an email, Share Battling misinformation through health messaging to Facebook, Share Battling misinformation through health messaging to Twitter, Share Battling misinformation through health messaging on LinkedIn, Share Battling misinformation through health messaging in an email, Share Health misinformation video to Facebook, Share Health misinformation video to Twitter, Share Health misinformation video on LinkedIn, Share Health misinformation video in an email, Battling misinformation through health messaging. Whether this person notices or not, they might be providing an inaccurate or manipulated picture to confirm a specific conclusion. 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. You can be the judge. On Sept. 29, 2015, Republicans from the U.S. Congress questioned Cecile Richards, the president of Planned Parenthood, regarding the misappropriation of $500 million in annual federal funding. The ASA continued, Because we understood that another competitors brand was recommended almost as much as the Colgate brand by the dentists surveyed, we concluded that the claim misleadingly implied 80 percent of dentists recommend Colgate toothpaste in preference to all other brands. The ASA also claimed that the scripts used for the survey informed the participants that the study was being performed by an independent research company, which was inherently false. For example, the objective graph literacy scale is a test with 13 items. The results provide deceiving information that creates false narratives around a topic. Why most published research findings are false. What is a conclusion you could draw from this plot that would not make much sense (i.e., pushing them to make the causation error)? However, a closer look shows that the X-axis starts at 420,000 instead of 0. Quasi-experimental, single-center, before and after studies are enthusiastically performed. 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. Under the CCSSM, beginning in the seventh grade, students are expected make comparisons between different samples on the same attribute. This is with the same aim of making it seem like the cases are dropping. If the sample size of the study is too small to prove its conclusion then you should be responsible enough and not use these results as an absolute truth as this paves the way for future misinformation. Lets take a look at some of the evidence for and against. The most common ways statistics are misused, besides misinterpretation, are the following: faulty polling, flawed correlations, misleading data visuals, selective bias and small sample size (Lebeid 2018). Scientists! In 2012, the global mean temperature was measured at 58.2 degrees. Provide training and resources for grantees working in communities disproportionately affected by misinformation (e.g., areas with lower vaccine confidence). (Citation2012) titled Case Studies for Quantitative Reasoning: A Casebook of Media Articles. 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. It also happens to be a topic that is vigorously endorsed by both opponents and proponents via studies. Now, the obvious answer is going for option A. In the sections that follow we will show two cases of widely disseminated data visualizations that (mis)represent the situation they are describing. Example #1. We also discuss the possible source/motivations behind such (mis)representation of the data. Omitting the baseline. Uncover the power of spider charts with this complete guide including examples, best practices, and more! Basically, there is no problem pro se - but there can be. Moreover, in both the Pre-K12 and College Report of the Guidelines for Assessment and Instruction in Statistics Education documents (Bargagliotti etal. But you cannot know until you ask yourself a couple of questions and analyze the results you have in between your hands. In the image above, we can see a graph showing 77% of Christian Americans in 2009, a number that decreased to 65% in 2019. Annual Data 3. 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. Consider headlines and images that inform rather than shock or provoke. This is a useful way to show how the use of two vertical axes can aid in visualizing association between two phenomena, particularly because the two vertical axes are different unitsallowing for a more accurate comparison.
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