ABSTRACT
Statistical inference is a broad topic and the people go over the very basics using polls as a motivating example. Forecasting the election is a more complex process since it involves combining results from 50 states and DC. In this chapter, the authors show how the probability concepts they learned in the chapter can be applied to develop the statistical approaches that make polls an effective tool. To help the reader understand the connection between polls and what they have learned, let’s construct a similar situation to the one pollsters face. Conducting an opinion poll is being modeled as taking a random sample from an urn. The ideas presented on how the people estimate parameters, and provide insights into how good these estimates are, extrapolate to many data science tasks. The CLT tells the reader that the distribution function for a sum of draws is approximately normal. Confidence intervals are a very useful concept widely employed by data analysts.
