# Sampling

## Sampling

This topic teaches students about the relationship between a sample and the larger population from which it is taken. Students learn about sampling distributions, and the accuracy of studies using samples of different sizes. Students learn about a number of related issues including how samples are selected, sampling distributions, the central limit theorem, and the question of how we infer generalizable results from sample estimates.

Topic Learning Outcome: Students will be able to clearly explain why sampling is necessary for statistical analysis and will have working knowledge of how samples are selected, what sampling distributions are, and how we infer results from sample estimates.

Core Concepts associated with this Topic: Central Limit Theorem.

Moore, D., McCabe G., & Craig, B. 2009. “Introduction to the Practice of Statistics, Sixth Edition.” New York: W. H. Freeman and Company. Chapter 5.

Syllabus Section: Sampling Distributions for Proportions

Moore, David S., George P. McCabe, and Bruce Craig. 2014. “Introduction to the Practice of Statistics.” 8th Edition. Sections 3.3 (pp. 202-206) and 5.2 (pp. 312-326).

Syllabus Section: Sampling and the Sampling Distribution

Healey, Joseph F. 2013. “The Essentials of Statistics: A Tool for Social Research.” 2nd edition. Wadsworth/Cengage Learning. Chapter 7 (pp. 146-154).

Syllabus Sections: Research Designs; Sampling Basics; Displaying Data and Sampling Distributions

De Veaux, Richard D., Paul F. Velleman, and David E. Bock. 2011. “Stats: Data and Models.” 3rd Edition. Boston: Pearson Education. Chapters 1-3, 12-13, 18.

Possible Assessment Questions:

1.)   What is simple random sampling? Describe an alterntive sample technique.

2.)   Define the term “sampling distribution.”

3.)   What is the central limit theorem? Why is this concept important?

4.)   What is a sampling error?

Page created by Ben Eisen and Sean Goertzen on 4 November 2014

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