Choose one of the fallacies below. You must choose a different topic for each as
ID: 390962 • Letter: C
Question
Choose one of the fallacies below. You must choose a different topic for each assignment.
Write a 1-2 page paper describing it:
1 inch margins, top, bottom, and sides
11 point Calibri font
Use the Normal style in Microsoft Word. This is equivalent to single spaced paragraphs with a double space between paragraphs.
Content:
Describe the fallacy or issue. What mistake is typically made? Why is it incorrect?
Give an examples of the fallacy or issue.
Describe a question, problem, choice, etc.
Indicate how the fallacy might manifest, and why it is incorrect.
Describe how the fallacy could be avoided.
Repeat for a second example.
Your grade isn't based on whether you checked off these requirements. Your grade is based on whether your paper tells me that you understand the fallacy, you can recognize when it is occurring, and you know how to avoid it.
Save your assignment as a MS Word document or pdf.
Topics:
Anscombe's Quartet (think of what the data sets are meant to demonstrate)
Availability Heuristic
Base-Rate Fallacy
Conjunction Fallacy
Confirmation Bias
Gambler's Fallacy
McNamara Fallacy
Post Hoc Fallacy
Regression Fallacy
Representativeness Heuristic
Sampling Bias
Simpson's Paradox
Spurious Correlation - this one will have to be really well done to get credit for it
Survivorship Bias
Explanation / Answer
Sampling Bias
According to the Business dictionary, sampling bias is defined as a systematic error in a statistical population sample omitting some members resulting in a biased sample with no equal balance and objective representation. Sampling bias occurs in the method of sampling. Normally a random sample is adopted in most of the surveys to arrive at predictions and results. The sampling bias occurs mostly in the random sampling method. The self-selection is a part of the sampling bias where the participants self-select themselves in participating in a survey. Consider the example of an internet-based online survey about social media use, the tech-savvy people from the major participants while others are not interested in the survey and do not take part in the survey. This self-selection results in a biased sample and the results of the online survey might go wrong. Under coverage of respondents is another sampling bias that is common in most of the sampling methods. The historic failure of the election poll conducted by Literary Digest can be cited as an example of under coverage in a sampling method. Non-response bias is another sampling bias where the respondents participate in the survey but do not submit their response to be considered in the survey.
The sampling bias manifests in a systematic manner with underestimation of a particular parameter in a population. Consider election poll example, the main question is who will win the presidential election? In this example under coverage of the sample, population lead to sampling bias. The respondents participate in the survey but do not return their responses to the survey resulting in poor results. An underestimation of a population parameter occurs and practically it is impossible to cover the entire population in an election poll. Also in the question, it cannot be said that the sample equally covers all age groups, gender, occupation and various other variables resulting in sampling bias and perfect randomness cannot be attained. In most of the election polls, the minority population and poor are underrepresented resulting in sampling bias. These mislead to scientific fraud and have a negative connotation. There are some people deliberately using the sampling bias to produce misleading information causing a panic in the public. The sampling bias in an election poll can lead to misrepresentation among the public and could play on their psychology that could have an impact on the original elections.
The sampling bias could be reduced if misrepresentation degree is small. The sample differs markedly could lead to a reasonable estimate of the election poll example. The sampling bias could be reduced by maintaining an equal representation from all segments involved as a sample. This could produce approximate estimates that are close to representing the entire population. Assigning sample weights could reduce the sampling bias considerably.