| Central limit theorem | If all samples of a specified size are selected from any population, the sampling distribution of the sample means is approximately a normal distribution. This approximation improves with larger samples.
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| Cluster sampling | A population is divided into clusters using naturally occurring geographic or other boundaries. Clusters are then randomly selected and a sample is collected by randomly selecting from each cluster.
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| Sampling distribution of the sample mean | A probability distribution of all possible sample means of a given sample size.
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| Sampling error | The difference between a sample statistic and its corresponding population parameter.
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| Simple random sample | A sample selected so that each item or person in the population has the same chance of being included.
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| Standard Error of the Sample Mean | The standard deviation of the sampling distribution of the sample means.
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| Stratified random sample | A population is divided into groups, called strata, and a sample is randomly selected from each stratum.
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| Systematic random sample | A random starting point is selected and then every kth member of the population is selected.
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