Episode Details

Almost all research and analysis in statistics is based upon sampling data, partly because it is expensive and time-consuming to get data on the entire population, and sometimes because it is the only feasible option. In this session, I look at the basics of sampling, starting with the contrast between random and non-random approaches, with a focus on how bias enters samples. I also look at sampling error, an unavoidable side product of sampling, and how increasing sample size can help in reducing that error.
Slides: http://www.stern.nyu.edu/~adamodar/pdfiles/Statistics101/Slides/Session2.pdf
Post class test: http://www.stern.nyu.edu/~adamodar/pdfiles/Statistics101/postclass/session2test.pdf
Post class test solution: http://www.stern.nyu.edu/~adamodar/pdfiles/Statistics101/postclass/session2soln.pdf
Webpage for statistics class: http://people.stern.nyu.edu/adamodar/New_Home_Page/webcaststatistics.htm
YouTube Playlist for this class: https://www.youtube.com/playlist?list=PLUkh9m2BorqmXcRzWFbzcjMd7fYErVexF

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