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As you read about studies in newspapers and magazines, the actual data that was collected, which could be thousands of data entries, are not included in the article. Rather, you will likely find a visual representation or graph of the data. Many times, these displays can be misleading or exaggerated to get you to think a certain way. For this discussion, you will conduct an internet search to find an article that includes a graph or visual display. You will analyze the graph or visual display to determine whether the graph is misleading, exaggerated, or misrepresents the data. You may want to review the reading in Section 2.3 under the heading of Misleading Graphs in your digital book. Then you will write a descriptive essay with a minimum of 5 paragraphs to address the following questions:

Consider what you know about misleading or exaggerated graphs.

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· When you looked at the graph for the first time, do you think it effectively showed the intended information? Why or why not?

· Answer one of the two questions below.

· Do you feel this graph was misleading in any way? Explain.

· If you feel the graph is not misleading, why do you feel it is appropriate? Explain.

· Would you have designed this graph differently? Such as design a different type, including other variables, or provide different labels? Why or why not?

Post 1: Initial Response

After you have found a graph or visual display in a news article, magazine, or internet story, draft your descriptive essay using the questions above as a guide. Do not simply answer each question, but put these ideas together into a cohesive descriptive essay with five paragraphs using the guidelines below. Once you have written your essay in a Word document, start a new thread post that includes a snippet of the graph you used, or a web link to the news article. Here is a video on how to use a snipping tool. Copy and paste the body of your essay, and attach the final version of your Word document as your initial response to this discussion.

Post 2: Reply to Classmate

Review both the visual display and writing your classmate posted. What are the similarities and differences between their view and your view? Explain.

Post 3: Reply to Another Classmate

Review the visual display that they found and discussed. Did you notice anything that they did not mention? What are two or three things that you will keep in mind when viewing visual displays from now on?

The essay should include: 1. Title page2. Reference page3. Be double-spaced4. Written in 12- point Times New Roman font. 5. You are expected to use at least one outside source for this essay 6. Cite outside sources in proper APA format7. Include a snippet of your graph or a link to your article along with your essay. 

POST 1

The article I choose for this discussion is A Spike in Lightning Strikes May Be Linked to Climate Change. I choose to look up lightning as I am fascinated with thunderstorms and this article is what I found to be interesting. What is interesting to me is that researchers reported that the frequency of lightning strikes has shot up in the last decade for the region. Data was collected using lightning sensors and they have placed 65 degrees N latitude, which cuts right through the middle of Alaska, as the Artic (Temming, 2021). I like the graph in this article because I feel they are being honest.

     When I looked at the graph for the first time, I do believe it effectively showed me the intended information because it was a good visualization of data needing to be presented. This graph shows you two different aspects of the data that was calculated for the graph. I believe using a bar graph and line graph both correctly represent the data categorizing a 10-year span of summertime lightning strikes, from 2010 to 2020. The X and Y-axis are labeled correctly, and the graph starts at zero. Since lightning strikes happen so fast, I also think the numbering system by 20,000s works great and the numbers are not too big or too small for the representation of the data.

     I feel this graph could have almost misrepresented the data and could have been a bit confusing. If the graph did not have a paragraph above it to explain what the orange line represented it would not have made sense to me. According to data collected by the Worldwide Lightning Location Network, it shows an increase in lightning strikes over the summertime in the Artic (Temming, 2021). The blue bar graphs show data collected from 2010 to 2020 with increasing the number of sensors used each year to collect the data. “Since that uptick may in part be due to the network adding more sensors, researchers calculated the number of lightning strikes they would have expected to see each year if the WWLLN used the same number of sensors over time (orange line)” (Temming, 2021).

     I would have designed this graph differently to make it a little bit easier to understand without needing to read the paragraph above to understand what it is trying to tell you. I would have maybe used a side-by-side bar graph comparing the results of data from 2010 to 2020. I would have the data collected over time with an increasing number of sensors over the years in blue bars. And the data that was calculated from 2010 to 2010 with the same number of sensors each year would be in the orange bar graph next to it.  I would have a legend on the graph to describe what each color meant.  Also, I would have included the temperature average over the summertime for the years to see if warmer days made a difference.

    A spike in lightning strikes may be due to climate change. I think they are trying to be honest here with the data in the graph because they showed calculated data (orange line) for the same number of sensors through the years instead of just leaving the blue bar graph with data from increasing sensors over time. They could have left people believing those said numbers, instead, they corrected it.

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POST2

2020: A Banner Year for Misleading Graphs

Last year was an overwhelming year when it came to statistics and graphs.  It seemed that news outlets were a 24/7 parade of graphs and statistics related to COVID-19.  Each media source would skew their graphs based on the political affiliation of their followers. 

I chose a graph that was published in a BBC news source.  The graph depicts the case ratio of death rates by age, medical condition and gender.  At first glance, this graph does a great job of conveying the point that persons over the age of 80 had a higher fatality rate and those with cardiovascular conditions also had a higher death rate per case.  It’s clearly obvious to the viewer which age group or medical condition was higher with the exaggerated bar graph.

When you examine the graph closer, along the x-axis shows a percentage of cases ranging from 0% to 15%.  This is extremely confusing and misleading.  The question is why would they not depict the x-axis as 0% to 100% (Cotgreave, 2020).  What is the significance of 15%?  Another issue is the total amount of cases isn’t shown. 

I think to get a better picture of the actual data, I would not have used percentages.  I probably would have still used a bar graph.  However, I would have used number of fatalities in either 100’s or 1000’s along the y-axis and represent age, medical condition or sex along the x-axis.  This would give a better representation of the data.  I realize this may not convey the point they are trying to make, though.

While this graph makes its point for the article, when you look at it closer it raises a lot of questions.  Readers need to examine these graphs closer and make educated conclusions based off of the data they are presented with.  2020 taught us a lot about science and statistics.  After examining this and some of the other graphs I have seen, one thing it has taught me is to look closer at misleading data in graphs.

Cotgreave, A. (2020, March 9). What the BBC got wrong in their COVID-19 visualization. Retrieved from Tableau: https://www.tableau.com/about/blog/2020/3/covid-19-resources-data-viz-best-practices

POST 3

When looking at studies we must understand that most are done using controlled environments. The studies also generally affect the results of the study using administered techniques. I always found this to be non-practical and is a way to find the results researchers want instead of letting the tests go uninterrupted and collecting true data. This is the case with the research on sleep and the immune systems function.  

                I researched a study on the number of hours an individual sleeps and their immune systems strength. In this study individuals were tracked using movement watches and tracking their actual hours of sleep. Then the participants were isolated and given the cold virus. It was reported that the individuals with less sleep were more likely to get a cold or illness.

                Now the graph showed this data in an easy-to-understand graph. I would not change the design of this graph. The graph shows the number of hours slept and the number of ill participants for that group of hours. I think it’s an accurate depiction of the data and it is made to be understood easily.

                However, the issue with accuracy in the sleep study is where I find this graph misleading. I believe that the first issue comes from the way the sleep was tracked. If a participant moved while sleeping the time was deducted from the hours of sleep logged. I find this in accurate, because many people tend to move or roll over in their sleep and never wake up. Therefore, the number of hours slept in my opinion was skewed.

                Secondly, they gave the virus to the participants and logged how many got sick. That is not a practical way to log illness. It would have been more accurate to have the participants encounter a sick person like you would in society then log if they got sick. Also, if they could look at the individuals’ medical records for the past year, they could have seen who gets sick more often. By doing a controlled study I find the results to be skewed in the favor of the researchers since this was the first study of its kind and every scientist wants to leave their mark.   

Hanae ArmitageSep. 1, 2.,

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