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examples of misleading statistics in healthcare

    examples of misleading statistics in healthcare

    73.6% of statistics are false. What Is A Misleading Statistic? Example #1. While a malicious intent to blur lines with misleading statistics will surely magnify bias, the intent is not necessary to create misunderstandings. Tufte (Citation2001) talked about this in his book, The Visual Display of Quantitative Information, making a point that having two vertical axes on a time series plot can be very useful when attempting to show a plausible association between two things. Ebola, for example, kills 50% of the people it infects on average, which is why the doctors who treat it wear hazmat suits. Provide training and resources for grantees working in communities disproportionately affected by misinformation (e.g., areas with lower vaccine confidence). Based on the misuse techniques we covered, it is safe to say that this sleight off-hand technique by Colgate is a clear example of misleading statistics in advertising, and would fall under faulty polling and outright bias. (, Comparing Box plot Distributions: A Teachers Reasoning, Enhancing Statistical Literacy: Enriching Our Society, Journal of Statistics and Data Science Education. 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. Likewise, what are the motives behind it? Verify the accuracy of information by checking with trustworthy and credible sources. This is one of the many controversial examples of misleading COVID statistics that illustrates the authorities' responsibility to inform their audience accurately. Since the ruling, it has apologised for the 'error'. 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. In this article, we review government power for, and constitutional limits on, regulating health-related speech, particularly on the internet . Was there a rapid decline in cases? 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. It usually falls down on the sample of people surveyed. Share sensitive information only on official, Misleading Coronavirus graphs. This is just one of many examples of misleading statistics in the media and politics. Luxembourg and Andorra are in the top 10 largely because of their exceptionally small populations (roughly 600,000 and 77,000, respectively). 5) How To Avoid & Identify The Misuse Of Statistics? The most common one is of course correlation versus causation, which always leaves out another (or two or three) factors that are the actual causation of the problem. Image: Yale University What's wrong with this picture? Under the CCSSM, beginning in the seventh grade, students are expected make comparisons between different samples on the same attribute. The time 7 million was 5x more than 6 million. Politifact, a fact-checking advocacy website, reviewed Rep. Chaffetzs numbers via a comparison with Planned Parenthoods own annual reports. Although in 2007 the company was forced to pay a $600 million fine for its criminal actions, the consequences of this are still seen to date. 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. False or misleading information is causing people to make decisions that could have dangerous consequences for their health. 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. Lets look at one of them closely. 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. Address health misinformation in your community by working with schools, community groups, and health care professionals to develop local strategies against misinformation. 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. To illustrate, a survey asks 20 people a yes-or-no question. 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? The below graph is the one most often referenced to disprove global warming. 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. As you saw throughout this post, illustrated with some insightful bad statistics examples, using data in a misleading way is very easy. Prioritize protecting health professionals and journalists from online harassment. What is a conclusion you could draw from this plot that would be more accurate (i.e., pushing them to consider association or correlation concepts)? 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. Independent university study group, lab-affiliated research team, consulting company? Sample size is especially important if you analyze results in terms . In this case 100/1.2% =88. Improper bubble sizes 13. Fact 1: The world's population is rapidly ageing. In this case, there is no way to know if the data were purposefully (mis)represented to support a particular message, or if it were (mis)represented by accident. They're infallible, concrete, impossible to argue with -- however you want to spin it, they make one solid point. On August 6, 2020, Rachel Maddow of MSNBC tweeted Chart: Kansas mask counties versus no-mask mandate counties (Maddow Citation2020, August 6) along with a link to a plot (see Figure 1) created by the Kansas Department of Health and Environmentwhich was also shared live on The Rachel Maddow Show that same day. Lets take a look at some of the evidence for and against. By Bernardita Calzon in Data Analysis, Jan 6th 2023, 3) Misleading Statistics Examples In Real Life. An example of misleading statistics is when determining whether to take a medical test for a rare but serious disease like spina bifida. Official websites use .govA .gov website belongs to an official government We also discuss the possible source/motivations behind such (mis)representation of the data. Staying with our example, that would look like this: Given the rising costs to the middle class, do you support government assistance programs?. 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. Scientists! The intent is to convey a shift in focus from cancer screenings to abortion. 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. Home Uncategorized examples of misleading statistics in healthcare. From there naturally stems the question: who paid them? The cases start growing rapidly, but since March 26, the growth seems to slow down and come closer to the top of the curve. Be prepared to be confused. Provide the public with context to avoid skewing their perceptions about ongoing debates on health topics. For example, the objective graph literacy scale is a test with 13 items. However, more often than not, data dredging is used to assume the existence of relationships without further study. Fig. Misleading graphs are a source of misinformation that worry many experts. 1. 2005;2 (8):e124. Learn everything there is to know about the power of professional area charts. 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. You can be drawn in by the good from what appears to be a reputable source and then can. For further thinking about this topic, I recommend this blogpost (Rost Citation2018, May). Regardless, many people will look at the graph and get a different idea of what the actual difference is, which is an unethical and dangerous practice. Learn how to identify and avoid sharing health misinformation. The field of statistics is concerned with collecting, analyzing, interpreting, and presenting data.. According to a definition by the Stanford Encyclopedia of Philosophy, a Simpsons Paradox is a statistical phenomenon where an association between two variables in a population emerges, disappears or reverses when the population is divided into subpopulations. For example, a misleading data visualization included in a financial report could cause investors to buy or sell shares of company stock. 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. 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. Statistical reliability is crucial in order to ensure the precision and validity of the analysis. This is reported by the makers of Fosamax accurately as a 56% reduction in risk, which is true but misleading. Another issue, and maybe the worst of them all, is that the dates under the bars are not ordered chronologically. 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. Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine. Use a broader range of credible sourcesparticularly local sources. It is fixed". Which saw an increase of millions of visitors in just a couple of years, so far, everything looks normal. Given the importance of data in todays rapidly evolving digital world, it is important to be familiar with the basics of misleading statistics and oversight. Looking for U.S. government information and services? 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. These examples bring up several concepts that are, under the Common Core State Standards for Mathematics (CCSSM) (NGAC & CCSSO 2010), introduced beginning in the sixth grade, such as understanding differences between histograms and bar charts, as well as drawing comparisons between two samples, leading to an understanding of association (for both continuous data and categorical data) and correlation. 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. Examples of Misleading Statistics in Healthcare 1. Institute of Medicine (US) Committee on Quality of Health Care in America. For instance, showing a value for 3 months can show radically different trends than showing it over a year. Nutrition studies have a particularly bad reputation in the news. This rare disease causes the spine of a baby to form improperly and can lead to serious mobility impairments and possible organ malfunctions. Cherry picking data. Another common misuse of statistics is strategically picking the time period to show a result. . 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. Take care to apply data responsibly, ethically, and visually, and watch your transparent corporate identity grow. An official website of the United States government. Moreover, we believe these kinds of examples are useful in expanding the toolkit of resources available that are in line with other similar resources, such as the book published by Madison etal. 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. This is a Simpsons Paradox at its finest, and it happens when the data hides a conditional variable that can significantly influence the results. 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. please save N95s and surgical masks for our healthcare workers who . Basically, there is no problem pro se - but there can be. Statistics can tell us about trends that are. Cherry Picking 2. Proactively address information deficits. Now, if we take a closer look at this chart we can find a few mistakes that make the information very misleading. Why most published research findings are false. 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. Moreover, this is a common topic appearing in tertiary introductory statistics courses, as well as courses on quantitative reasoning. Statistics are infamous for their ability and potential to exist as misleading and bad data. The prevalence of health misinformation was the highest on Twitter and on issues related to smoking products and drugs. In 2006, The Times, a popular UK newspaper, printed a story about how they were the leading paper both online and in print in the UK. Collecting data from too small a group can skew your survey and test results. Truncating axes is a very dangerous false statistics practice, as it can help create wrong narratives around important topics. For example, starting the axes in a predefined value so that it will affect the way the graph is perceived to achieve a certain conclusion. People who were more susceptible to . Mixing up linear and logarithmic scales. Strengthen and scale the use of evidence-based educational programs that build resilience to misinformation. 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. What did the scientist or statisticians try to figure out? It is easy to see a correlation. Citation2020; GAISE College Report ASA Revision Committee Citation2016), there are specific goals related to being a critical consumer or informed citizenwhich includes being able to dissect and make sense of statistical information designed for the general publicas well as content expectations around facility with accurate data visualizations. Editors, clients, and people want something new, not something they know; thats why we often end up with an amplification phenomenon that gets echoed and more than it should. The first example of misleading data visualization comes to us courtesy of Reddit but was originally propagated by Fox news. Accepted author version posted online: 12 Apr 2021, Register to receive personalised research and resources by email. 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. However, some have argued that it may have been unintentional (Cairo Citation2020, May 20). 1. The example above is an example of selective bias; the biologists were recruited, not randomly selected. 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. Misinformation spreads especially easily on social media and online retail sites, as well as via search engines. 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). - Do you think that the government should help those people who cannot find work? Statistics are nfi for to ability and capability to existing as misleading and bad data. That means there will likely be six possible explanations: - Car accidents (A) cause bear attacks (B), - Bear attacks (B) cause car accidents (A), - Car accidents (A) and bear attacks (B) partly cause each other, - Car accidents (A) and bear attacks (B) are caused by a third factor (C), - Bear attacks (B) are caused by a third factor (C) which correlates to car accidents (A). Seasonal flu, meanwhile, only kills around 0.1%. In CCSSM, students gain experiences with histograms beginning in grade 6, and they begin comparing multiple plots as early as the seventh grade. Cumulative VS. A plot with two vertical axes is inherently more complicated to digest, especially in this case, because the two axes are not designed to show a relationship between two different attributes. While numbers dont lie, they can in fact be used to mislead with half-truths. Omitting the baseline. After showing this plot to students, some useful questions could be: Fig. While numbers dont always have to be fabricated or misleading, it is clear that even societys most trusted numerical gatekeepers are not immune to the carelessness and bias that can arise with statistical interpretation processes. Therefore, using the first graph, and only the first graph, to disprove global warming is a perfect misleading statistics example. If youre not sure, dont share. Our guide included some misleading examples and illustrations of data, several of which come from the Reddit thread for misleading visual statistics. And over the years, tobacco. Specific wording patterns have a persuasive effect and induce respondents to answer in a predictable manner. 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. They can lead to misleading statistics that give you a faulty idea of customer satisfaction and product preferences. 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). You can see the updated version below. It is a data mining technique where extremely large volumes of data are analyzed for the purpose of discovering relationships between different points. As an entrepreneur and former consultant, Mark Suster advises in an article, you should wonder who did the primary research of said analysis. At the first glance, there may appear to not be anything inherently misleading about this plot (see Figure 1). While certain topics listed here are likely to stir emotion depending on ones point of view, their inclusion is for data demonstration purposes only. If you see this graph, you would obviously think the UKs national debt is higher than ever. The birth rate for . To illustrate this point further, lets assume that a study has found a correlation between an increase in car accidents in the state of New York in the month of June (A), and an increase in bear attacks in the state of New York in the month of June (B). The time an upside down y-axis made "Stand Your Ground" seem much more reasonable. Assess the impact of health misinformation. Small samples underrepresent your target audience. Many seem wilfully false, created out of, say, a journalist's desire to create a sensation, a government's need to make a political point or an aid agency's wish for more funds. A 22-page overview of health misinformation and resources to stop it. Well, a Simpsons Paradox can happen when an analyst doesnt look at the complete scope of the data. Registered in England & Wales No. It becomes hard to believe any analysis! Christopher Engledowl & Travis Weiland wrote an insightful article called Data (Mis)representation and COVID-19: Leveraging Misleading Data Visualizations For Developing Statistical Literacy Across Grades 616. Example 8: Urban Planning. 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. Thats whats going on in your organization.. Misleading pie chart 4. However, closer inspection reveals that the dates along the horizontal axis are not in order of time, with, for instance, May 1 appearing before April 30 and April 26 appearing in between May 7 (on the left) and May 3 (on the right). 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. Misleading statistics refers to the misuse of numerical data either intentionally or by error. A slideshow version of the Community Toolkit for educators and other community leaders. Annual Data 3. 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? Now, the obvious answer is going for option A. The ASA stated that the claim would be understood by readers to mean that 80 percent of dentists recommend Colgate over and above other brands, and the remaining 20 percent would recommend different brands.. 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. secure websites. The graph was later republished with organized dates and counties. Until March 26, the bars' heights correspond to the numbers. Health Misinformation Current Priorities of the U.S. Health (2 days ago) Office of the U.S. 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. ) or https:// means youve safely connected However, some survival rate statistics can be misleading because they don't take into account differences in patient characteristics, such as age, sex, and stage of disease. In the sections that follow we will show two cases of widely disseminated data visualizations that (mis)represent the situation they are describing. 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 lack of statistical literacy from the public, paired with the fact that organizations didnt always share accurate statistical information, lead to widespread misrepresentation of data. Now, you might be wondering, how can this be misleading? . 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). Brian Kemp's said: "The x-axis was set up that way to show descending values to more easily demonstrate peak values and counties on those dates, our mission failed. Manipulating the Y-axis+ 6. Source #1: A small sample size. Purposely or not, the time periods we choose to portray will affect the way viewers perceive the data. I have mentioned the most common mistakes that can lead to misleading or misuse of statistics. The top 10 most-shared articles that were reviewed by clinicians and scientists for accuracy were: The article, titled The Times leaves the rest behind started by displaying a graphic of the exponential growth of The Times website visitors from 2004 to 2006. Amplify communications from trusted messengers and subject matter experts. Making this a clear example of how the time period that we chose to portray can significantly change the way people will perceive the information. The above graph/chart was presented as a point of emphasis. It is worth mentioning that 1998 was one of the hottest years on record due to an abnormally strong El Nio wind current. Here are five techniques for fudging the numbers with misleading statistics examples: Technique #1: Citing Misleading "Averages" The first technique is using the word "average" without specifying what kind of average a figure represents. Evaluate the effectiveness of internal policies and practices in addressing misinformation and be transparent with findings. In addition to our cases motivating discussion of association, the plots also offer an important consideration of how scaling modifications can mislead the consumer. You can see a graph that shows the UK National debt from 1995 to 2016. Look at the About Us page on the website to see if you can trust the source. When this paradox goes unnoticed, it can significantly influence the way the data is interpreted, leaving room to believe a certain conclusion or assumption is an absolute truth, when it could change by looking at it from a different perspective. And finally, if youre not sure about the content dont share it. This is an open access article distributed under the terms of the Creative Commons CC BY license, which permits unrestricted use, distribution, reproduction in any medium, provided the original work is properly cited.

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