Section 4
Sampling Distributions
By Boundless
![Thumbnail](../../../../../../figures.boundless-cdn.com/18029/square/4803461-2a14d12c19-o.jpeg)
The sampling distribution of a statistic is the distribution of the statistic for all possible samples from the same population of a given size.
Knowledge of the sampling distribution can be very useful in making inferences about the overall population.
![Thumbnail](../../../../../../figures.boundless-cdn.com/18025/square/-05-08-20at-201.31.23-20pm.jpeg)
Learn to create a sampling distribution from a discrete set of data.
![Thumbnail](../../../../../../figures.boundless-cdn.com/18027/raw/boxplot-vs-pdf.jpg)
When we have a truly continuous distribution, it is not only impractical but actually impossible to enumerate all possible outcomes.
![Thumbnail](../../../../../../figures.boundless-cdn.com/18065/square/figure1.gif)
The mean of the distribution of differences between sample means is equal to the difference between population means.
![Thumbnail](../../../../../../figures.boundless-cdn.com/18062/raw/standard-deviation-diagram.jpg)
The overall shape of a sampling distribution is expected to be symmetric and approximately normal.
![Thumbnail](../../../../../../figures.boundless-cdn.com/18064/square/mpirical-clt-figure-040711.jpeg)
The central limit theorem for sample means states that as larger samples are drawn, the sample means form their own normal distribution.