Sample Mean

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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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