Sampling error and non-sampling error.

Sampling error and non-sampling error are two types of errors that can occur in statistical analysis. Understanding the difference between these two types of errors is important for any researcher who conducts surveys, experiments or collects data for research purposes.

Sampling error refers to the error that occurs when the results of a sample are used to make inferences about a larger population. Sampling error is the difference between the sample statistic and the population parameter it is intended to estimate. Sampling error can arise due to random variation or sampling bias. Random variation is the natural variation that occurs in any sample, while sampling bias refers to the systematic errors that arise when the sample is not representative of the population.

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