 Sampling: Final and Initial Sample Size Determination

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Sampling: Final and Initial Sample Size Determination
Chapter Twelve Sampling: Final and Initial Sample Size Determination

Symbols for Population and Sample Variables
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Sample vs. Population Population parameters are unknown
Sample statistics are used as estimates of parameters Population parameters are fixed Sample statistics change from sample to sample If simple random samples of size n are drawn from a population with mean µ and variance 2, then when n is large, the sample mean will be approximately normally distributed with mean equal to µ and variance equal to 2/n (known as standard error of the mean).

Definitions and Symbols
Standard error of the estimate: Standard deviation of the parameter to be estimated in the research. Precision level: When estimating a population parameter by using a sample statistic, the precision level is the desired size of the estimating interval. This is the maximum permissible difference between the sample statistic and the population parameter. Confidence level: The confidence level is the desired probability that a confidence interval will include the population parameter.

Finding Probabilities Corresponding to Known Values
Area is Z Scale

The Confidence Interval Approach
Note that is estimated by . The confidence interval is given by We can now set a 95% confidence interval around the sample mean of \$182. As a first step, we compute the standard error of the mean: From Table 2 in the Appendix of Statistical Tables, it can be seen that the central 95% of the normal distribution lies within z values. The 95% confidence interval is given by + 1.96 = (3.18) = Thus the 95% confidence interval ranges from \$ to \$ The probability of finding the true population mean to be within \$ and \$ is 95%.

Sample Size Determination for Means and Proportions
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