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DESCRIPTION: \n\n\n\n	\n	 \n\n	Title: The Law of Large Populations: The return
  of the long-ignored N and how it can affect our 2020 vision\n\n	Abstract: 
 For over a century now\, we statisticians have successfully convinced ours
 elves and almost everyone else\, that in statistical inference the size of
  the population N can be ignored\, especially when it is large.  Instead\,
  we focused on the size of the sample\, n\, the key driving force for both
  the Law of Large Numbers and the Central Limit Theorem. We were thus taug
 ht that the statistical error (standard error) goes down with n typically 
 at the rate of 1/√n.   However\, all these rely on the presumption that ou
 r data have perfect quality\, in the sense of being equivalent to a probab
 ilistic sample.  A largely overlooked statistical identity\, a potential c
 ounterpart to the Euler identity in mathematics\, reveals a Law of Large P
 opulations (LLP)\, a law that we should be all afraid of. That is\, once w
 e lose control over data quality\, the systematic error (bias) in the usua
 l estimators\, relative to the benchmarking standard error from simple ran
 dom sampling\, goes up with N at the rate of √N.   The coefficient in fron
 t of √N can be viewed as a data defect index\, which is the simple Pearson
  correlation between the reporting/recording indicator and the value repor
 ted/recorded.  Because of the multiplier√N\, a seemingly tiny correlation\
 , say\, 0.005\, can have detrimental effect on the quality of inference.  
 Without understanding of this LLP\,  “big data” can do more harm than good
  because of the drastically inflated precision assessment hence a gross ov
 erconfidence\, setting us up to be caught by surprise when the reality unf
 olds\, as we all experienced during the 2016 US presidential election. Dat
 a from Cooperative Congressional Election Study (CCES\, conducted by Steph
 en Ansolabehere\, Douglas River and others\, and analyzed by Shiro Kuriwak
 i)\,   are used to estimate the data defect index for the 2016 US election
 \, with the aim to gain a clearer vision for the 2020 election and beyond.
 \n\n	 \n\n	 \n\n
DTSTART:20180216T203000Z
DTEND:20180216T213000Z
LOCATION:Room 217\, Maass Chemistry Building\, CA\, QC\, Montreal\, H3A 0B8
 \, 801 rue Sherbrooke Ouest
SUMMARY:Xiao-Li Meng  Whipple V. N. Jones Professor of Statistics Harvard U
 niversity
URL:/mathstat/channels/event/xiao-li-meng-whipple-v-n-
 jones-professor-statistics-harvard-university-284646
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