(BQ) Part 2 book "Essential statistics - Exploring the world through data" has contents: Survey sampling and inference, hypothesis testing for population proportions, inferring population means, analyzing categorical variables and interpreting research. | 7 Survey Sampling and Inference 324 324 03/09/16 4:24 pm THEME If survey subjects are chosen randomly, then we can use their answers to infer how the entire population would answer. We can also quantify how far off our estimate is likely to be. S omewhere in your town or city, possibly at this very moment, people are participating in a survey. Perhaps they are filling out a customer satisfaction card at a restaurant. Maybe their television is automatically transmitting information about which show is being watched so that marketers can estimate how many people are viewing their ads. They may even be text messaging in response to a television survey. Most of you will receive at least one phone call from a survey company that will ask whether you are satisfied with local government services or plan to vote for one candidate over another. The information gathered by these surveys is used to piece together, bit by bit, a picture of the larger world. You’ve reached a pivotal point in the text. In this chapter, the data summary techniques you learned in Chapters 2 and 3, the probability you learned about in Chapter 5, and the Normal distribution, which you studied in Chapter 6, are all combined to enable us to generalize what we learn about a small sample to a larger group. Politicians rely on surveys of 1000 voters not because they care how those 1000 individuals will vote. Surveys are important to politicians only if they help them learn about all potential voters. In this and later chapters, we study ways to understand and measure just how reliable this projection from sample to the larger world is. Whenever we draw a conclusion about a large group based on observations of some parts of that group, we are making an inference. Inferential reasoning lies at the foundation of science but is far from foolproof. As the following case study illustrates, when we make an inference, we can never
