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The term "sampling error" has also been used in a related but fundamentally different sense in the field of genetics; ...
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All colored circles are included in the target population. Green and Orange colored circles are included in the sample frame. Green colored circles are a randomly generated sample from the sample frame. The sample frame includes overcoverage because John and Jack are the same person, but he is included more than once in the sample frame.
The sample mean could serve as a good estimator of the population mean. Then we have: The difference between the height of each man in the sample and the unobservable population mean is a statistical error, whereas; The difference between the height of each man in the sample and the observable sample mean is a residual.
In statistical hypothesis testing, a type I error, or a false positive, is the rejection of the null hypothesis when it is actually true. A type II error, or a false negative, is the failure to reject a null hypothesis that is actually false. [1] Type I error: an innocent person may be convicted. Type II error: a guilty person may be not convicted.
For a confidence level, there is a corresponding confidence interval about the mean , that is, the interval [, +] within which values of should fall with probability . ...
Sampling error, which occurs in sample surveys but not censuses results from the variability inherent in using a randomly selected fraction of the population for estimation. Nonsampling error, which occurs in surveys and censuses alike, is the sum of all other errors, including errors in frame construction, sample selection, data collection ...
Olivier, Bousquet; Luxburg, Ulrike; Rätsch, Gunnar, eds. (2004). Advanced Lectures on Machine Learning.Lecture Notes in Computer Science. Vol. 3176. pp. 169– 207 ...