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Beyond random.

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A stratified sample results when a population is separated into two or more subgroups, called strata, and simple random samples are selected from each strata in proportion to the relative size of the strata.

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Splitting the population into similar parts or clusters can make sampling more practical. Then we could select one or a few clusters at random and perform a census within each of them. If each cluster fairly represents the full population, cluster sampling will give us an unbiased sample.

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When a sample is to be selected from a list it is sometimes easiest to select one individual at random, and then count from the first individual a fixed number to pick the next individual. For example, the random number 134 is chosen, select the 134 th individual on the list for the sample. Then if the random number 20 was chosen, the 154 th individual on the list would be the next one chosen. With these numbers, what positions would the third, fourth, and fifth chosen individual’s position be? Assume there are only 200 individuals on the list.

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Uses data that is easily or readily available. For example, standing in one place and choosing the first 20 people who walk by. Polling only your friends about their favorite bands. Asking only your family about proper bed times for children.

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