What is source that analyzes misleading statistical claim

Assignment Help Management Theories

Reference no: EM133323590

Case Study: Statistics are a way of summarizing large data sets and making sense of them. Statistical results allow us to make decisions and test our preconceived opinions. While this makes statistics a powerful tool, it also means improper use can lead to misunderstanding data and making incorrect decisions. When people are trying to convince others that their arguments are the correct ones, they will use statistics to support their side. When winning is more important than the truth, they may intentionally present incorrect results and apply methodologies improperly.

The article above discusses some of the ways statistics can be used improperly to mislead other into believing one side against another. The article not only explains some common ways of doing this, but it also gives some real-life examples. Read the article linked above and then answer the discussion topic questions.

For this discussion, find one example where someone is misrepresenting data through improper use of statistics to support their viewpoint. Since identification of misleading statistics use can be difficult and tricky, you must search for a case where a reliable source has identified someone misusing a statistic to mislead. Do not try to identify an example of misuse yourself. You may use articles on statistics oriented websites or fact-checking websites to find your example.

Tips on finding proper examples:

Prioritize non-political examples. You can search for articles on, or examples of the categories mentioned in the article that is the basis of this discussion. A very popular category is Faulty/misleading data visualization. Some others are Faulty polling, Flawed correlations, Data fishing, Selective bias, Using percentage change in combination with a small sample size. Some topic options include: Nutrition, Health, Drugs, Advertising, Science, and Research.

Do not use examples from social media. Random people making faulty claims are not appropriate examples, regardless of how popularly circulated they may be.

Tips on responding to the questions:

Categorize the example you found according to the six categories described in the article. Since the hard work of identifying an attempt to mislead is already done by the analytical source, you will be evaluated on your selection of an example and subsequent analysis of the issue.

When responding to others, please only discuss whether you think the analysis was indeed misrepresentative or not. Do not discuss the overall claim that is being made and if you disagree with it. We are only concerned about the analysis methods, not the actual assertions made. Misleading statistics are intentionally used to push narratives; we are not here to argue anything but statistics.

Please use the template below in your answers, so everyone can easily follow your answers to all the questions (using the template below is part of the requirements; you will lose points if you do not follow the template or if you skip portions of what is being asked)

Question: What is the source that analyzes the misleading statistical claim?

What is the source or title of the original misleading claim?

What is the claim being made? Copy/paste or summarize the claim.

If it is a visual (like a chart) then please include the chart in the post. You can usually copy paste images into the post. Include as much of the information here as possible so all readers can see what you are describing without having to visit the link and search for the problem.

What is the statistical analysis proposed to support this claim? Copy/paste their description of how they arrived at their results.

Which category does this issue fall under? Why?

This is the most important part of the discussion. You demonstrate your understanding of the types of misleading statistics in this section. Justify your selection and your identification of the issue. For example, if you say "faulty polling" you need to cite the improper wording. If you say "data fishing" you need to show they are looking for correlations without a proper hypothesis.

What would be a better analysis to evaluate the situation? Describe how to fix the faults in this analysis or suggest a different approach.

Reference no: EM133323590

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