Expert Answer:Similarity/Likeability: Statistical Error Project

  

Solved by verified expert:Attached you will find the word document that needs to be filled out. The descriptions and examples of the error are not to be plagiarized from peers or the internet.I have included the definition for each error and you need to add the following:A description of eachAn example of eachI have included a sample entry below to better explain the process and what I expect. Please make sure that description and examples are clearly described.Statistical Error: Algorithm AversionDefinition: Evidence-based algorithms more accurately predict the future than do human forecasters. Yet when forecasters are deciding whether to use a human forecaster or a statistical algorithm, they often choose the human forecaster.Description: We like to rely on human judgment. people more quickly lose confidence in algorithmic than human forecasters after seeing them make the same mistake.Example: An example is people who don’t leave during hurricanes. Even though there is sophisticated analysis detailing weather patterns and the likelihood of a storm hitting a particular area, some people ‘go with their gut’ and stay at home.
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Statistical Error Project
Statistical Error: Similarity/Likeability
Definition: This is a systematic cognitive error humans make when judging others. We tend to be
drawn to and have better opinions of those who are similar to us.
Description:
Example:
Statistical Error: Sample not representative (too small)
Definition: The sample drawn to represent the population is too small in comparison to the
population. This is a problem because it can give an inaccurate estimate of the distribution (i.e.
how the numbers vary within the population)
Description:
Example:
Statistical Error: Sample not representative (not random)
Definition: This occurs when all individuals, or instances, were not equally likely to have been
selected. This results in a biased sample.
Description:
Example:

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