# 7: The Central Limit Theorem

- Page ID
- 20692

In this chapter, you will study means and the **central limit theorem**, which is one of the most powerful and useful ideas in all of statistics. There are two alternative forms of the theorem, and both alternatives are concerned with drawing finite samples size *n* from a population with a known mean, \(\mu\), and a known standard deviation, \(\sigma\). The first alternative says that if we collect samples of size \(n\) with a "large enough \(n\)," calculate each sample's mean, and create a histogram of those means, then the resulting histogram will tend to have an approximate normal bell shape. The second alternative says that if we again collect samples of size \(n\) that are "large enough," calculate the sum of each sample and create a histogram, then the resulting histogram will again tend to have a normal bell-shape.

- 7.1: Prelude to the Central Limit Theorem
- The central limit theorem states that, given certain conditions, the arithmetic mean of a sufficiently large number of iterates of independent random variables, each with a well-defined expected value and well-defined variance, will be approximately normally distributed.

- 7.2: The Central Limit Theorem for Sample Means (Averages)
- In a population whose distribution may be known or unknown, if the size (n) of samples is sufficiently large, the distribution of the sample means will be approximately normal. The mean of the sample means will equal the population mean. The standard deviation of the distribution of the sample means, called the standard error of the mean, is equal to the population standard deviation divided by the square root of the sample size (n).

- 7.3: The Central Limit Theorem for Sums
- The central limit theorem tells us that for a population with any distribution, the distribution of the sums for the sample means approaches a normal distribution as the sample size increases. In other words, if the sample size is large enough, the distribution of the sums can be approximated by a normal distribution even if the original population is not normally distributed.

- 7.4: Using the Central Limit Theorem
- The central limit theorem can be used to illustrate the law of large numbers. The law of large numbers states that the larger the sample size you take from a population, the closer the sample mean <x> gets to μ . The central limit theorem illustrates the law of large numbers.

- 7.5: The Sample Proportion
- Often sampling is done in order to estimate the proportion of a population that has a specific characteristic.

- 7.E: Sampling Distributions (Exercises)
- These are homework exercises to accompany the Textmap created for "Introductory Statistics" by Shafer and Zhang.

- 7.E: The Central Limit Theorem (Exercises)
- These are homework exercises to accompany the Textmap created for "Introductory Statistics" by OpenStax. Complementary General Chemistry question banks can be found for other Textmaps and can be accessed here. In addition to these publicly available questions, access to private problems bank for use in exams and homework is available to faculty only on an individual basis; please contact Delmar Larsen for an account with access permission.

Barbara Illowsky and Susan Dean (De Anza College) with many other contributing authors. Content produced by OpenStax College is licensed under a Creative Commons Attribution License 4.0 license. Download for free at http://cnx.org/contents/30189442-699...b91b9de@18.114.