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These are quick descriptions of some of the topics available at the workshops.
Starting with the everyday meaning of “normal,” we’ll use data to construct an operational definition of “normal.” One example will make use of a census of all 4-million registered births over a year in the US, including birth-by-birth measures of pregnancy risk factors, maternal and newborn outcomes, and demographics. This will seque into more abstract measures of “normal,” such as the shape and parameters of the famous normal distribution.
How big a sample?
We’ll start with real ballot-by-ballot data from which we can draw random samples. By comparing results from a sample to the actual results, we’ll determine whether and to what extent a sample can be representative and predictive.
Which way did the probability go?
Using medical diagnosis and outcomes data, you’ll show students the distinction between the likelihood of an outcome given a diagnosis and the accuracy of the diagnosis itself.