Using charts and graphs to display data is easier for the brain to process than by pouring over reports.
Respond to at least two of your classmates by commenting on their posts. Do you agree with their visualization comparisons? Why or why not? Though two replies are the basic expectation for class discussions, for deeper engagement and learning you are encouraged to provide responses to any comments or questions others have given to you. Continuing to engage with peers and the instructor will further the conversation and provide you with opportunities to demonstrate your content expertise, critical thinking, and real-world experiences with the discussion topics.
Brianna Mortel
2:14pm
Mar 11 at 2:14pm
Using charts and graphs to display data is easier for the brain to process than by pouring over reports. It gives a clearer, more concise idea of what the data means through visualization. For this reason, tables are a preferred visualization technique when each column (variable) identifies the What and each row (case/record) identifies the Who. This allows a group of similar data to be arranged in a way that is legible and does not need interpretation. In the below image below (Sharpe, 2019), you can see the columns and the rows to easily understand this table:
Five different charting techniques that are available for visualization include:Bar Chart – it’s a graph with rectangular bars with heights associated with the values they represent. It is especially useful to compare different categories. Bar charts make comparing these data points especially easy to interpret. Bar charts are best used when there are large differences between data.Segmented Bar Charts – Also called a stacked bar chart is used for grouping the parts of data as a whole by placing them on top of one another adding up to 100% of the value. For example, you have a classroom of 100 students. 25 of them prefer math, 40 of them prefer science, 15 of them prefer literature, and 20 of them prefer history, you can stack these 4 groups onto each other to add up to 100 while still identifying each preferred group.Mosaic Plot – Adds additional information to a bar chart. “Each rectangle in a mosaic plot is proportional to the number of cases corresponding to that combination of variable levels” (Sharpe, 2019). This is the most complex of all the 5 charts listed here which can make it far more difficult to interpret. Pie Chart – is a circle shaped graph where each data is represented by a slice of the pie. The size of the slice is determined by the size of the data in comparison to the rest. It is a great way to show percentages and proportions. It does make it hard to accurately determine all data size as each slice is at angles and the labels are generally clustered.
Ring Chart – it’s a variant of pie charts but shows the data as a ring, versus the entire pie shape. It is visually appealing as the title of the chart is in the middle of the ring, so you clearly know what your data represents. Simpson’s Paradox is “a phenomenon that arises when averages, or percentages, are taken across different groups, and these group averages appear to contradict the overall averages” (Sharpe et al, 2019). With this paradox, pertinent data disappears when combined with a larger group. One famous example of the Simpson’s paradox occurred at UC Berkeley regarding their suspected gender-bias. According to Grigg (2019), In 1973, Berkeley’s admitted 44% of male applicants and 35% of female applicants which led to the school being sued for gender discrimination. UC Berkeley knew this wasn’t the case, so they hired a statistician named Peter Bickel to look at their data. Peter found that “there was a statistically significant gender bias in favour of women for 4 out the 6 departments, and no significant gender bias in the remaining 2” (Grigg, 2019). They found, after surveying women, that women tended to apply to the department that admitted a smaller percentage of applicants overall. Because the percentage was smaller, when added to a group, it was minimized by the larger percentages making it appear that the school was gender biased towards men.
Resources
Sharpe, N. D., De Veaux, R. D., & Velleman, P. F. (2019). Business statistics (4th ed.). Retrieved from https://www.redshelf.com (Links to an external site.)
Grigg, T. (2019, January 08). Simpson\’s paradox and interpreting data. Retrieved from https://towardsdatascience.com/simpsons-paradox-and-interpreting-data-6a0443516765
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