English

Siddharth Kalla66.8K reads

Sampling distributions are an important part of study for a variety of reasons. In most cases, the feasibility of an experiment dictates the sample size. Sampling distribution is the probability distribution of a sample of a population instead of the entire population.

Discover 23 more articles on this topic

Don't miss these related articles:

- 1What is Sampling?
- 2Basic Concepts
- 3Sampling
- 4Probability Sampling
- 5Non-Probability Sampling

In simpler words, suppose from a given population you take all possible samples of size n and compute a statistic (say mean) of all these samples. If you then prepare a probability distribution of this statistic, you will get a sampling distribution.

The properties of sampling distribution can vary depending on how small the sample is as compared to the population. The population is assumed to be normally distributed as is generally the case. If the sample size is large enough, the sampling distribution will also be nearly normal.

If this is the case, then the sampling distribution can be totally determined by two values - the mean and the standard deviation. These two parameters are important to compute for the sampling distribution if we are given the normal distribution of the entire population.

Sampling distribution of the mean is obtained by taking the statistic under study of the sample to be the mean. The say to compute this is to take all possible samples of sizes n from the population of size N and then plot the probability distribution. It can be shown that the mean of the sampling distribution is in fact the mean of the population.

The standard deviation however is different for the sampling distribution as compared to the population. If the population is large enough, this is given by:

Where σ is the mean of the population and σx̄ is the population mean.

These formulas are only valid when the population is normally distributed. If this is not the case, then the mean and standard deviation of the sampling distribution will be different and will depend on the type of distribution of the population.

The normal distribution is one of the simplest probability distributions and so it is quite easy to study and analyze. We can easily find mathematical formulas for the sampling distribution statistics that we want to find.

However, when the distribution is not normal, it can be quite complicated and such easy mathematical formulations might be hard to find or even impossible in some cases. In those cases, we use approximate methods because finding the exact value will entail studying every single sample of size n taken from the population, which is very hard and time consuming.

Full reference:

Siddharth Kalla (Aug 20, 2011). Sampling Distribution. Retrieved Jul 23, 2024 from Explorable.com: https://explorable.com/sampling-distribution

The text in this article is licensed under the Creative Commons-License Attribution 4.0 International (CC BY 4.0).

This means you're free to copy, share and adapt any parts (or all) of the text in the article, as long as you give ** appropriate credit** and

That is it. You don't need our permission to copy the article; just include a link/reference back to this page. You can use it freely (with some kind of link), and we're also okay with people reprinting in publications like books, blogs, newsletters, course-material, papers, wikipedia and presentations (with clear attribution).

Discover 23 more articles on this topic

Don't miss these related articles:

- 1What is Sampling?
- 2Basic Concepts
- 3Sampling
- 4Probability Sampling
- 5Non-Probability Sampling

Footer

Thank you to...

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 827736.

Explorable.com - 2008-2024

You are free to copy, share and adapt any text in the article, as long as you give *appropriate credit* and *provide a link/reference* to this page.