In statistics, a sampling distribution or finite-sample distribution is the probability distribution of a given random-sample-based statistic. If an arbitrarily large number of samples, each involving multiple observations, were separately used in order to compute one value of a statistic for each sample, then the sampling distribution is the probability distribution of the values that the statistic takes on. In many contexts, only one sample is observed, but the sampling distribution can be found theoretically.

A sampling distribution is a probability distribution of a statistic obtained through a large number of samples drawn from a specific population. The sampling distribution of a given population is the distribution of frequencies of a range of different outcomes that could possibly occur for a statistic of a population.

For example, suppose you wanted to find out the sampling distribution of SAT scores for all U.S. high school students in a given year. To do so, you would take repeated random samples of high school students from the general population and then compute the average test score for each sample. The distribution of those sample means would provide you with the sampling distribution for the average SAT test score.

onlinelibrary.wiley.com [PDF]

… Essentially, the procedure is to generate uniformly distributed random numbers, order the series from 1 … in our simulations searches through, say 50 series, we use the sampling properties of … We also use our sample of potential instruments to calibrate the parameters that govern …

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… Essentially, the procedure is to generate uniformly distributed random numbers, order the series from 1 … in our simulations searches through, say 50 series, we use the sampling properties of … We also use our sample of potential instruments to calibrate the parameters that govern …

link.springer.com [PDF]

… Essentially, the procedure is to generate uniformly distributed random numbers, order the series from 1 … in our simulations searches through, say 50 series, we use the sampling properties of … We also use our sample of potential instruments to calibrate the parameters that govern …

www.sciencedirect.com [PDF]

… Essentially, the procedure is to generate uniformly distributed random numbers, order the series from 1 … in our simulations searches through, say 50 series, we use the sampling properties of … We also use our sample of potential instruments to calibrate the parameters that govern …

onlinelibrary.wiley.com [PDF]

… Essentially, the procedure is to generate uniformly distributed random numbers, order the series from 1 … in our simulations searches through, say 50 series, we use the sampling properties of … We also use our sample of potential instruments to calibrate the parameters that govern …

www.sciencedirect.com [PDF]

… Essentially, the procedure is to generate uniformly distributed random numbers, order the series from 1 … in our simulations searches through, say 50 series, we use the sampling properties of … We also use our sample of potential instruments to calibrate the parameters that govern …

academic.oup.com [PDF]

… Essentially, the procedure is to generate uniformly distributed random numbers, order the series from 1 … in our simulations searches through, say 50 series, we use the sampling properties of … We also use our sample of potential instruments to calibrate the parameters that govern …

www.jstor.org [PDF]

… Essentially, the procedure is to generate uniformly distributed random numbers, order the series from 1 … in our simulations searches through, say 50 series, we use the sampling properties of … We also use our sample of potential instruments to calibrate the parameters that govern …

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