Sampling Error
Sampling error is the difference between a population parameter and a sample statistic that results from random sampling. It occurs because a sample gives incomplete information about a population. Sampling error can be reduced by increasing the sample size, using a representative sample, and minimizing non-sampling errors. Non-sampling errors can arise from various sources such as measurement errors, data processing errors, and non-response errors. Sampling error is an important concept in statistics and data science as it affects the accuracy and reliability of statistical inference and hypothesis testing.
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