Link of the website:https://alg.manifoldapp.org/api/proxy/ingestion_sources/339ee58b-7d40-4bc2-aa1d-0061aa89bf09

Last week we explored how improper methods of sampling can cause data to be misleading. These methods often happen behind the scenes and are rarely noticed by consumers of data. In fact, it is quite difficult to access the source data that data distributors use.

Statistics are misrepresented in much more obvious ways. In this module you have explored many ways data can be misrepresented. Often partisan organizations will provide misleading graphics to justify arguments that are minimally supported by the data. The public, despite possessing common sense, will likely believe these graphics, even though they are logically flawed.

Take a look at the graphs in A Short Tour of Bad Graphsby C. J Schwarz of Simon Fraser University. Once you have reviewed these graphs, write a brief response, organized by the following points

Which graph was the most misleading? Be sure to reference the section that contains the graph (for example, graph 4.6).

Do you think this graphical error was intentional? What reasons do you think contributed to this misleading statistic?

Would another method be more suitable to represent this data? How would you represent this data accurately?

Ideally your post should be in essay format. Try to write an introductory paragraph and three paragraphs that answer the questions above, followed by a conclusion. We'll keep this format throughout the course in future discussions, as we'll learn that effective writing and critical thinking often go hand-in-hand.

Instructions:

Your initial post (approximately 200-250 words) should address each question in the discussion directions