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    Statistics - Simple Random Sampling & Cluster Sampling

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    Compare the differences of simple random sampling and cluster sampling using specific examples, and show the advantages and disadvantages of each method. What are the advantages and disadvantages of systemic random sampling and stratified random sampling?

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    (A) Simple random sampling is a technique in which each unit of the population has the same chance of being included in the sample. The bias of the investigator is kept out and the inclusion of one unit into the sample does not influence the inclusion or non-inclusion of the others. Therefore the sample is a true representative of the population.

    (a) Personal bias in the section process is totally eliminated.
    (b) The larger the sample size, more closely it represents the population.
    (c) Sampling errors are governed by the principles of chance and therefore the analyst may easily verify the accuracy of his estimates.

    (a) It may be difficult to define the population from which random samples are to be drawn. An unrestricted population makes sampling tedious.
    (b) Large size samples are required for reliability of results and this increases the cost of sampling.

    Cluster sampling is a technique in which sampling happens in two or more stages. First-stage ...

    Solution Summary

    This solution provides a detailed explanation of simple random sampling, cluster sampling, systemic random sampling, and stratified random sampling. Additionally, the solution outlines the strengths and weaknesses of all four of these sampling types.