Question

Find two (2) examples of "badly" designed graphs from outside sources. They can be from a recent magazine article, a book, a research paper, or a website. If the source is printed material, you can scan or take a photograph of it. If it is from a website, you can do a screen capture.

Requirements:
a) For each graph, describe in detail what is wrong with the graph.
b) Choose just one of your "bad" graphs and re-do it either by sketching your revised version on paper and then photographing it, or by writing a Python script and using Matplotlib. Fix all of the things that are wrong with the original graph. You may change the type of graph if that enhances understanding of the data.
c) Speculate on the context of your chosen graphs. Do you believe the results were caused by simple carelessness or a misunderstanding of how graphs should be made, or do you think there was a deliberate attempt to show the graph in a biased way? Write a few sentences that justifies your opinion.

Here are additional requirements:
- Each graph should be from a different source, e.g., one from a book/magazine and another from a website. Cite your source (i.e., where it came from).
- One graph should be relatively recent (created after Jan. 1, 2020). The other graph can be newer or older.
- Only submit graphs that are substantially bad! Each graph should have at least two major things wrong with it. The worse, the better!

Answer 1

Poorly designed graphs can mislead due to scaling issues, inappropriate graph types, clutter, or bad color choices. Fixing these involves starting scales at zero, choosing suitable graph types, balancing information, and clear color differentiation. The intent behind badly designed graphs could be either carelessness or an attempt to bias the data.

Step-by-step explanation:

It can be quite informative to analyze poorly designed graphs, as it helps to understand the importance of accurate and clear data representation. In the case of a badly designed graph, there could be several flaws, such as:

• Using misleading scales that don't start at zero, which can exaggerate or minimize differences.
• Choosing inappropriate graph types that don't represent the data well.
• Including too much or too little information, making the graph cluttered or overly simplistic.
• Bad use of color or patterns that make the graph difficult to read or interpret.

To rectify such issues, one could:

1. Ensure all scales start at zero to give an accurate sense of proportion.
2. Select a graph type that fits the data, like a bar chart for categorical data or a line graph for trending over time.
3. Balance the amount of information to make the graph informative but not overwhelming.
4. Use clear and distinct colors or patterns to differentiate data sets easily.

When encountering a graph with errors, it might be due to carelessness or lack of understanding in graph design. However, it's also possible that the creator might have intentionally presented the data in a misleading way to support a specific narrative.