Sampling Distribution

Sampling distribution can be defined as a probability distribution of the statistics that is derived by drawing a large number of samples from a specific and particular population. There are a number of different frequencies within a sampling distribution that indicate a number of different outcomes that may come by drawing a number of different samples from the same population. This means the sampling distribution can be done for a statistic of a particular population.

The example of sampling distribution can be given as suppose we draw all the possible samples from a given population that can be drawn. All these samples are of uniform size called n. Now we calculate different calculations by using these samples. That mean we calculate means, median, mode and standard deviation by using these samples. The probability distribution of all these samples is called sampling distribution.

The best use of sampling distribution is to use this distribution to test a hypothesis. The practical example of the use of the sampling distribution can be done by supposition that you want to find out the SAT scores of all the students in all of the high schools of USA. There will be a general population of the students from which you will take the random samples of high school students of USA. After taking the sample you will find out the mean of the samples or the average test score of the each sample of the high school students. The distribution of those samples means that you are getting the sampling distribution of the average SAT test score of the high school students of USA.

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