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Understanding Data: Analysis, Skepticism, and Epistemology

Explore the concepts of data analysis, skepticism, and epistemology to understand how to avoid being a sucker or looking like a fool. Discover the sources and criteria of knowledge, and learn how to trust and verify the accuracy of data. This course will equip you with the skills to conduct appropriate and informed data analysis.

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Understanding Data: Analysis, Skepticism, and Epistemology

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  1. How not to be a sucker or look like a fool Analysis of Biological Data Dr. Ryan McEwan Department of Biology University of Dayton ryan.mcewan@udayton.edu

  2. What can be known?

  3. Epistemology: What can be known? • truth? belief? • Skepticism • Sources and criteria of knowledge

  4. In Class Exercise: • As an individual • - Write a definition of data. • What give an example of some data that use on a regular basis. • How much do you trust the data? • How do you know the data you use are accurate? What are you data?

  5. In Class Exercise: • As an individual • - Write a definition of data. • What give an example of some data that use on a regular basis. • How much do you trust the data? • How do you know the data you use are accurate? • As an group • - Compare your definitions of data: draft a single definition. • Share your examples- what about these data sources are common? What differences? • Do you all trust the others data sources? • What are you sources of skepticism? What are you data?

  6. Americans are VERY good at understanding data

  7. http://www.espn.com/nba/story/_/page/tripledoubletracker/follow-russell-westbrook-triple-double-chasehttp://www.espn.com/nba/story/_/page/tripledoubletracker/follow-russell-westbrook-triple-double-chase

  8. https://www.nytimes.com/interactive/2016/04/16/upshot/stephen-curry-golden-state-warriors-3-pointers.html?_r=0https://www.nytimes.com/interactive/2016/04/16/upshot/stephen-curry-golden-state-warriors-3-pointers.html?_r=0

  9. For example, consider the following differences based on 2015 statistics from the World Health Organization (WHO):9 • The lifetime risk of maternal death is 1 in 11 in Afghanistan — compared to 1 in 17,800 in Ireland. • In the US, African Americans represent only 12 percent of the population, but account for almost half of all new HIV infections. • More than 80 percent of noncommunicable diseases are in low- and middle-income countries. • In London, when travelling east from Westminster toward Canning Town, each tube stop represents nearly one year of life expectancy lost. • In Japan, life expectancy at birth is more than 80 years; in several African countries, it's fewer than 50 years.

  10. http://www-personal.umich.edu/~mejn/election/2016/

  11. https://fivethirtyeight.com/features/one-ohio-countys-struggles-are-fueling-trump-support/https://fivethirtyeight.com/features/one-ohio-countys-struggles-are-fueling-trump-support/

  12. https://www.truecar.com/used-cars-for-sale/listing/5TEJU4GN7AZ698013/2010-toyota-tacoma/https://www.truecar.com/used-cars-for-sale/listing/5TEJU4GN7AZ698013/2010-toyota-tacoma/

  13. https://bigfuture.collegeboard.org/college-university-search/university-of-daytonhttps://bigfuture.collegeboard.org/college-university-search/university-of-dayton

  14. https://www.climate.gov/

  15. https://www.nytimes.com/interactive/2017/01/22/us/politics/womens-march-trump-crowd-estimates.htmlhttps://www.nytimes.com/interactive/2017/01/22/us/politics/womens-march-trump-crowd-estimates.html

  16. In Class Exercise: • As an individual • - Write a definition of data. • What give an example of some data that use on a regular basis. • How much do you trust the data? • How do you know the data you use are accurate? • As an group • - Compare your definitions of data: draft a single definition. • Share your examples- what about these data sources are common? What differences? • Do you all trust the others data sources? • What are you sources of skepticism? • Regroup! • -How do you want to change your definition? What are you data?

  17. First Principle: As a scientist, investigator or data handler, it is your personal responsibility to make sure that the analysis you are doing is appropriate. If you have a collaborator on the project who is a statistician, that is dandy, however, you should be able to explain the analysis, or else couch it in your own ignorance. If you do not have a statistician working directly with you, then you need to commit yourself to grinding out the proper analysis. Read some stats books! Look for analogous analyses in the literature, check out some on-line resources. This course, and any other course you can take, will only give you “doorways” or concepts for analysis that you can apply…it is up to you to verify, explore and execute

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