Criminal Justice

1- In the video, “Don’t Be Fooled by Bad Statistics”

1- what were the three forms of bad statistics mentioned, and why is it important they are avoided?
2- What are some examples of each?
3- While not called by this in the video, what was the “Michael Jordan Fallacy”, how was it related to statistics, and is there a solution?

Your response should be in APA format, 12pt font, 1″ margins, references page, etc. Cover page and abstract not required. Make sure to use a .doc, Docx, or RTF file format and attach the file when submitting.

This assignment has a minimum of five (5) required sources. While the video can count as one source, this means you need to find additional sources to add detail and support to your answers. Look for real-world examples in the news, peer-reviewed journals, etc.

ANSWER

Don’t Be Fooled by Bad Statistics

Don’t Be Fooled by Bad Statistics

What were the three forms of bad statistics mentioned, and why is it important they are avoided? What are some examples of each?

Poorly collected data is the first type of bad statistics mentioned in the video (Dressler, 2010). Poor data collection results in the production of wrong research results. During research on which magazine has more readers, the researchers made calls to the participants of the study during working hours. This meant that stay-at-home mothers were the most likely respondents for the study (Dressler, 2010). Therefore, the research findings were a result that only applied to the stay-at-home mothers’ group. Researchers must accommodate all variables present concerning the average of the object or issue they are studying. Poor data collection can result in wastage of research resources, misleading research results, inability to validate research questions properly, and inability to answer the research questions.

Leading questions are another form of bad statistics. Leading questions can result in research participants giving false responses (Baxter et al., 2006; Suh, 2015). For example, if a researcher tells respondents that the use of smartphones has been proven to cause brain damage, asking them if they agree or not, a majority of the respondents are likely to agree that it does. The responses would be different if the researcher simply asks respondents whether they believe that the use of smartphones causes brain damage. Researchers should be careful when wording the research questions so as not to appear to lean towards a certain form of response, as respondents tend to try to align with leading questions during research (Baxter et al., 2006; Ritchie et al., 2013).

The final bad statistic mentioned is the misuse of the data center when calculating the average (Dressler, 2010). In most cases, people use the mean when calculating the average of a set of data. However, outliers can influence the mean (Dressler, 2010). It is, therefore, important that the median is sometimes used. The video gives an example of how Michael Jordan gets a degree in geography but fails to fit in with the other graduates when their incomes are graphed. This is because he has an extremely high pay from being a professional basketballer (Dressler, 2010). Jordan is the outlier in this example. Including Jordan’s income in the calculation of the average income for geography graduates would result in a figure that would not be accurate.

While not called by this in the video, what was the “Michael Jordan Fallacy”, how was it related to statistics, and is there a solution?

The “Michael Jordan Fallacy” is portrayed in the example given in the video. A comparison of geography graduates and their incomes now shows a consistent average, even though Michael Jordan is an outlier in the data (Dressler, 2010). Including Jordan’s income figures when calculating the mean of the incomes would result in an average that does not accurately reflect the income of geography graduates. This shows that outliers in a set of data have a significant impact on the average. Using mean may, therefore, not be appropriate. A solution to this problem is to use the median. The median presents an accurate measure of the center of data and is not impacted by outliers (Manikandan, 2011). This means that the median would be a better measure to use when a data set has outliers.

References

Baxter, J. S., Boon, J. C., & Marley, C. (2006). Interrogative pressure and responses to minimally leading questions. Personality and Individual Differences, 40(1), 87-98.

Dressler, E. (2010, February 27). Don’t Be Fooled By Bad Statistics. https://www.youtube.com/watch?v=jguYUbcIv8c

Manikandan, S. (2011). Measures of central tendency: Median and mode. Journal of pharmacology and pharmacotherapeutics, 2(3), 214.

Ritchie, J., Lewis, J., Nicholls, C. M., & Ormston, R. (Eds.). (2013). Qualitative research practice: A guide for social science students and researchers. sage.

Suh, M. (2015, January 29). Questionnaire design. http://www.pewresearch.org/methodology/u-s-survey-research/questionnaire-design/.