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Job and health insurance losses accelerating!

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Job and health insurance losses accelerating!

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At first this graph does not seem that misleading until you notice that there are two graphs being plotted on the same graph. On the left side, the y-axis is labeled as uninsured Americans and on the right side, the y-axis is labeled as unemployment rate. The story the graph is trying to portray is that uninsured Americans and unemployment rates are increasing at the same rate. Another issue with the fact that there are two graphs on the same y-axis scaling is that the right side is in percentages while the left side is in millions so the scaling for the two graphs are not actually the same. By doing the math, you can see that the increase in uninsured Americans is very small from 15% to 16% while the unemployment rate increases a lot more from 4.5% to 7.5%.

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This image focuses on risks without giving the audience all the information. Contacting or even seeing a bat is rare. Transmission of zoonotic from bats is rare, yet this is almost the only thing we hear about them. People have a societal fear of bats because of these warnings. They rarely describe what to do if you encounter a bat to keep yourself and this animal safe. They rarely describe the importance of bats to our environment. This is from a news site in Utah and I do not know the demographics of this audience, but this graphic is unfairly describing these animals and doesn’t provide much information besides risks which only builds fear rather than knowledge.

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This graph shows the supposed labor force participation rate of women and claims to be data pulled from the Bureau of Labor Statistics. It shows that women’s labor force participation rate has consistently increased from about 66% to almost 85% from 1950-2020. This graph is grossly misleading because the y-axis shifts from 65% to 90% despite the participation rate for women not ever being between those two percentages in the last 70 years. According to the Bureau of Labor Statistics, the participation rate for women in 1950 was actually only between 33-34%, not anywhere close to 65%. In 2020, the participation rate was actually around 55-56%, not 85% like the graph claims. Furthermore, the participation rate for women has never been higher than 61%. Overall, this graph is completely false and not anywhere near the truth of what the labor force participation rate has ever looked for women.

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Honestly, this graph has way too much going on for me to even begin to read it. To try to include all 50 states on one graph and only label them by color is far too muddles, and there are too many states labelled in blue that I'm not entirely sure which state is supposed to be the one that's spiking in cases. Additionally, this graph is working in gross numbers which can also make analysis on which states are best addressing the spread of COVID more difficult that if it was represented differently, but mostly the biggest issue in this graph, from my point of view, is that it's an overload of information that makes it difficult to glean anything from the graph.

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This graph demonstrates the average annual global temperature between 1880 and 2015. At first glance, the temperature change appears "insignificant" due to the flat trend. However, if one takes a closer look, it becomes clear the y-axis scale is unreasonable. The y-axis ranges from -10 degrees to 110 degrees Fahrenheit. During the last ice age, the average global temperature was about 46 degrees Fahrenheit, there is no reason for the y-axis to start at -10 degrees. The large-scale attempts to conceal the increasing trend that demonstrates the rising temperatures since 1880. Although the data used appears to be accurate, the graph's scale was intentionally chosen to mislead the audience and support the idea that climate change is a hoax.

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This graph is comparing the cost(?) of people on welfare vs. people with a full time job. The disparity between the two bar sizes is trying to convince the reader that people on welfare are costing(?) us so much more money. The judgement value assigned to this in our society is that these people are a burden. Given that the graph is on Fox news, as a reader I am aware of their negative views on social safety nets such as the welfare program. The graph lacks units of measure on the vertical axis.  If the bars were scaled correctly to the units, they would actually be very close in size, as the welfare bar is actually only about 7 million more dollars than the job bar. Additionally, there is no context in the graphs header or labeling to know what this dollar amount represents. I am assuming it is cost to taxpayers, but it's actually not clear just by looking at the graph. The graph's design is intended to provoke an emotional/ angry response in its viewers, as they might be mislead to believe that people on welfare are so, so costly to our society.

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This graph is trying to show a dramatic increase in high school diplomas during Obama's presidency. Although there is a gradual increase in the amount of diplomas by using books not drawn to scale they make it appear as if the increase was dramatic. Both of the 75% have 5 books but at 78% the graphic has 10 books. A difference of 5 books while the difference between those two percentages is only 3%. This kind of scaling gives the wrong impression and makes the growth to be more than it actually is.

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This visualization shows the number of gun deaths in Florida. At first glance, it looks like gun deaths decreased dramatically after Florida enacted the Stand Your Ground law in 2005, but really the y-axis is the opposite of how it usually is and the numbers are increasing from top to bottom instead of bottom to top. Technically this data is all correct, but the conventions of reading graphs make us mislead ourselves at first glance. Really, gun deaths increased dramatically after the passage of the stand your ground law, which the graph does show, but it is unclear when you first look at it.

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This graph is trying to prove its creator's argument that climate actually hasn't changed significantly overtime, so it isn't something we need to be concerned about. However, if you take a closer look there are a few things that make it confusing and misleading. First, the title says the average is 14C but then the graph points to the 0.5C on both sides. Also, if you look at the x-axis, you'll see they only pulled data from 1997-2012 (15 years). Of course the change is not going to look as dramatic from a smaller time period rather than over a century or more. The graph misleads audiences to think climate change is not "real".

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Number of COVID cases in 5 counties in Georgia

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Number of COVID cases in 5 counties in Georgia

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The visual above was created to show the top 5 counties in Georgia with the greatest number of confirmed COVID cases. At first glance, there doesn't seem to be anything wrong with this graph. There is a title and a small caption below the title that explains what the graph is representing. There is also a legend that helps the reader distinguish the counties. However, when you take a closer look at the x-axis, we can see that the dates are out of order. The creator of this graph most likely placed the dates out of order to make it seem like the number of confirmed cases was decreasing. We can make this conclusion because the way our visual looks, we can see that the number of cases do seem like they are decreasing each day. This graph also did not provide titles for the axes so it can be difficult for the reader to understand what they are measuring or what it is they are keeping track of.

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Counties with lowest and highest kidney cancer death rates

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Counties with lowest and highest kidney cancer death rates

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This visual displays two maps of the US in which the LOWEST and HIGHEST kidney cancer death rates are shown in the left and right, respectively. On the left, you see that the lowest death rates are in rural areas. This may make some people draw conclusions about how the rural lifestyle may affect people's kidneys. But as you look on the right, you see that the highest death rates are also in rural areas. So we see that there is clearly a disconnect and these two charts are both misleading. How is it that both the lowest and highest death rates are in rural areas and are so close to each other. This would cause people to make misleading conclusions about the visualizations.

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This graph is from a 2015 Planned Parenthood congressional hearing. It was prepared by the organization: Americans United For Life. It uses data from Planned Parenthood’s annual reports. However, it is a grossly inaccurate visualization of this phenomenon. If you look closely, there is no y-axis to scale any of the numbers consistently. The slope of these graphs look the same in magnitude, and there appears to be an intersection where abortions begin to occur more than cancer screening and prevention services. However, looking at the actual numbers, this is a complete lie. When graphed on a consistent y-axis scale, the increase in abortions is very limited, while the decrease in cancer screenings and prevention is slightly more pronounced, but there is no intersection of the two lines. Clearly, the pro-life organization was attempting to tell a skewed story about the relationship between abortions and life-giving treatments using a vague visualization, but the data itself does not support this.

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Amount of Environmental Waste Created and Recycled

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Amount of Environmental Waste Created and Recycled

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This visualization is supposed to depict the amount of environmental waste over the years 2000 to 2013.  The visualization is a misrepresentation of the data.  First we need to look at how the axis's are labeled.  The x-axis is not number sequentially.  various years are skipped not helping us understand the true trend of the data.  The y-axis also does not start at zero which could potentially skew how we see the data.  Th elimination of the data below 1,500,000 makes it seem as the year 2000 had very little trash.  We also do not know what the actual weight of the trash is, there is no unit of measure indicated.  The way the graph is represented can also be confusing for readers.  Combining the recycling and trash into one bar is hard to understand. Was recycling that high or is it just the difference in the two green measurements? Another question I have is is this waste from a certain city, state, country?

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Which Country is to Blame for Climate Change? 

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Which Country is to Blame for Climate Change? 

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This visual displays which country emits the most greenhouse gas emission in 2018. I found it interesting that the visual shows that China emits the most carbon dioxide, looks like almost doubling the amount emitted by the United States. However, the display is misleading due to the fact that China has a population of more than 1.4 trillion people, while US only has a population of 330 million people (per google search). China has more than 4 times as many people as the US. I believe that a per-capita basis graph would be a more accurate graph to show who is more responsible for emitting carbon dioxide and causing climate change, and I believe US would be the highest in that regard. So if we want other countries to work on their emission for climate change, we need to look at ourselves and start first.

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While the stats in this infographic are not necessarily incorrect, I found it interesting that the larger, bolded information is always the disorder, and not the information on the people experiencing it. Additionally, this graphic has the old, more inequitable language of "commit suicide." This language originates from when suicide was considered a crime, and it still is in many countries. And historically, it's been considered a moral sin in christian religions. Continuing to use this outdated language stigmatizes depression and other disorders that can lead to suicide. The generally accepted, more equitable language is "die by suicide." This language coupled with the bolded information seems to create a graphic more meant to insinuate panic in readers, and not encourage people to fund support for those who need it. Additionally, I found in interesting that the only intersectional statistic on the graphic was about substance abuse and mental health disorders. While this is a large issue, there are many other intersectional stats considering race, class, access, queer identities, etc. that could have been highlighted.

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I found this image as I was scrolling through Twitter the other day, and noticed this data story. The green US map on the left is what the Center for Disease Control posted on their website as “Community Levels”. The colorful US map on the right is what the Twitter user (@ JasonSalemi) published using the same data called “Community Transmission Levels”. The CDC map on the left is misleading for a few reasons. First, look at the color of the map. It is green which always indicates to humans that everything is all good. Meaning, the distinct use of the green color has implicit bias when showing the data. Next, pay attention to the scaling at the bottom. There are only three levels and they do not show what those colors represent in terms of numbers, which means it is very likely that the green range is much larger than the yellow or red range. Lastly, notice the title of the map. It isn’t called “Transmission” or “Infection Rates”, it simply is labeled “Community” which does not tell us much about what is truly being shown in the map. The map on the right is not perfect either, but definitely tries to counter this bad data story.

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This visualization of education costs for 17-year-olds packs in a lot of information in one diagram and is very misleading. I immediately noticed that there are two y-axes for one x-axis. The left vertical axis displays the per-pupil cost and the right axis represents the percent change in achievement although it fails to state what the measure of achievement is. Using the context from the 2009 Digest of Educational Statistics, we see that the cost figure increases are incorrect. This visual makes it appear as if the costs are tripled, but in reality, the costs are only doubled. The intentional labeling of the y-axis is manipulating the audience to believe that the cost is rapidly increasing throughout the year and the scores are remaining relatively the same. However, raw data shows that the math scores have a good return on the total investment in K-12 education.

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This visualization regarding climate models, is very misleading in multiple ways and a misinterpretation of global climate change. The observational and simulated model data are aligned at a single point at the start of the graph, in 1979. That choice serves to visually exaggerate any difference between the models and data. We also see that averages of multiple different observational data are put together. There are more than three groups that use satellite data to estimate the temperature of the atmosphere, so the graph is omitting some of the data that shows larger warming trends. The average elevation of the “bulk atmosphere” is 25,000 feet, which is below the peak of Mount Everest. The temperature at such high elevations isn’t very relevant to humans.

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The visualization shows the difference in annual pay for males and females to be 4% or less for the six featured jobs. The purpose of this visualization is to argue against the existence of wage gaps between genders. If critiquing solely the chart, then there does not seem to be any misrepresentation of data; however, looking at this topic in full raises a few concerns. First, why are those six specific professions chosen to represent the gender gap? Second, does this data represent males and females with the same qualifications and experiences? Third, the chart only accounts for the median annual salaries of these professions, but it does not consider that the gender wage gap also encompasses the idea that women with the same qualifications as men do not get promoted as quickly or as easily. Thus, the lack of information provided on this chart can be misleading as it creates a narrative that there is no gender wage gap despite cherry picking and leaving out important information that could change the context and conclusion of the study.