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Systematic sampling is a method of selecting a sample from a population in a systematic way.

Systematic sampling involves selecting every kth element from a population, where k is a constant. The first element is randomly selected from the first k elements, and then every kth element after that is selected until the desired sample size is reached. This method is useful when the population is too large to sample every element, but a representative sample is still needed.

For example, if a company wanted to survey their customers, but had a large customer base, they could use systematic sampling to select a representative sample. They could randomly select the first customer from the first 10 customers, and then select every 10th customer after that until they reach their desired sample size.

The formula for determining the sampling interval (k) is:

k = population size / sample size

For example, if a population has 1000 elements and a sample size of 100 is desired, the sampling interval would be:

k = 1000 / 100

k = 10

Therefore, every 10th element would be selected for the sample.

Systematic sampling can be biased if there is a pattern in the population that coincides with the sampling interval. To reduce this bias, a random starting point can be used, or the sampling interval can be changed.

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