Sampling and Data

Summary :

This opening chapter introduces the basic vocabulary of statistics and probability, noting that fields from economics and business to law and computer science all require at least one statistics course. It defines descriptive and inferential statistics, and covers how data are gathered and what distinguishes reliable data from unreliable data.

Why statistics matters across fields

The chapter motivates the subject by pointing out that statistical information appears constantly in newspapers, television, and the internet, covering topics from crime to real estate, and that fields as varied as economics, business, psychology, education, biology, law, computer science, and police science all require statistical literacy to interpret this information thoughtfully.

Descriptive versus inferential statistics

The science of statistics is defined as the collection, analysis, interpretation, and presentation of data, and the chapter splits the subject into descriptive statistics, which organizes and summarizes data through graphs and numerical summaries, and inferential statistics, which uses probability to draw and quantify confidence in conclusions about a larger population from sample data.

Gathering data and judging its quality

Because later statistical inference is only as good as the data behind it, the chapter covers how data are gathered, the basic ideas of sampling methods, and what separates good data from bad, giving students in the business track a foundation for evaluating survey results and other data before analyzing them further.