5290 WordsNov 8, 201222 Pages

Sampling and Sampling Distributions
7-1
Learning Objectives
In this chapter, you learn:
To distinguish between different sampling methods The concept of the sampling distribution To compute probabilities related to the sample mean and the sample proportion The importance of the Central Limit Theorem
7-2
Why Sample?
DCOVA
Selecting a sample is less time-consuming than selecting every item in the population (census).
An analysis of a sample is less cumbersome and more practical than an analysis of the entire population.
7-3
A Sampling Process Begins With A Sampling Frame DCOVA
The sampling frame is a listing of items that make up the population Frames are data sources such as population lists, directories, or*…show more content…*

Is the survey based on a probability sample? Coverage error – appropriate frame? Nonresponse error – follow up Measurement error – good questions elicit good responses Sampling error – always exists
7-14
Types of Survey Errors
Coverage error or selection bias
DCOVA
Exists if some groups are excluded from the frame and have no chance of being selected
Non response error or bias
People who do not respond may be different from those who do respond
Sampling error
Variation from sample to sample will always exist
Measurement error
Due to weaknesses in question design, respondent error, and interviewer’s effects on the respondent (“Hawthorne effect”)
7-15
Types of Survey Errors
DCOVA
(continued)
Coverage error Non response error Sampling error Measurement error
Excluded from frame Follow up on nonresponses Random differences from sample to sample Bad or leading question
7-16
Sampling Distributions
DCOVA
A sampling distribution is a distribution of all of the possible values of a sample statistic for a given size sample selected from a population. For example, suppose you sample 50 students from your college regarding their mean GPA. If you obtained many different samples of 50,

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