What Sampling error is
Sampling error is the difference between a sample statistic and the true population parameter. It is the result of taking a sample of a population instead of the entire population. Sampling error is unavoidable in any probability sample, and the size of the error can vary depending on the size of the sample and the characteristics of the population.
Steps for Sampling Error:
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Define a population from which a sample can be taken.
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Choose a sampling method (random sampling, stratified sampling, cluster sampling, etc.) and sample size.
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Collect the data from the sample.
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Calculate the statistic for the sample.
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Calculate the population parameter for the same statistic.
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Compare the sample statistic to the population parameter to determine the sampling error.
Examples
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An opinion poll of 1,000 people fails to accurately reflect the opinions of the entire population due to the fact that the sample size was too small.
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A survey of customers at a shopping mall fails to accurately reflect the views of the general population due to the fact that the sample was not representative of the whole population.
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A researcher fails to account for the fact that the sample was not randomly selected, leading to inaccurate results.