> For the complete documentation index, see [llms.txt](https://docs.abi.am/abi-omicss-guide-2021/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.abi.am/abi-omicss-guide-2021/week-4/statistics-and-r/central-limit-theorem.md).

# Central Limit Theorem

## edX Course

Visit the [edX course](https://learning.edx.org/course/course-v1:HarvardX+PH525.1x+3T2020/home), and complete the following:

1. [The Normal Distribution](https://learning.edx.org/course/course-v1:HarvardX+PH525.1x+3T2020/block-v1:HarvardX+PH525.1x+3T2020+type@sequential+block@d27d8d24b8c443c3acb9e8ac2877f99e/block-v1:HarvardX+PH525.1x+3T2020+type@vertical+block@0940279eeda74a5db377348992873755)
   * [Normal Distribution Exercises](https://learning.edx.org/course/course-v1:HarvardX+PH525.1x+3T2020/block-v1:HarvardX+PH525.1x+3T2020+type@sequential+block@d27d8d24b8c443c3acb9e8ac2877f99e/block-v1:HarvardX+PH525.1x+3T2020+type@vertical+block@e664f7059082463fa69c6ec64f310f9f)
2. [Populations, parameters, and sample estimates](https://learning.edx.org/course/course-v1:HarvardX+PH525.1x+3T2020/block-v1:HarvardX+PH525.1x+3T2020+type@sequential+block@d27d8d24b8c443c3acb9e8ac2877f99e/block-v1:HarvardX+PH525.1x+3T2020+type@vertical+block@74b2c3d1840f430db8017af8ceac0a62)
   * [Populations, Samples, Estimates exercises](https://learning.edx.org/course/course-v1:HarvardX+PH525.1x+3T2020/block-v1:HarvardX+PH525.1x+3T2020+type@sequential+block@d27d8d24b8c443c3acb9e8ac2877f99e/block-v1:HarvardX+PH525.1x+3T2020+type@vertical+block@df7325b3a65644a099270c95742ce62f)
3. [Central Limit Theorem (CLT)](https://learning.edx.org/course/course-v1:HarvardX+PH525.1x+3T2020/block-v1:HarvardX+PH525.1x+3T2020+type@sequential+block@d27d8d24b8c443c3acb9e8ac2877f99e/block-v1:HarvardX+PH525.1x+3T2020+type@vertical+block@6423da71519a492ab2f06387f9bf5aa7)
   * [Central Limit Theorem Exercises](https://learning.edx.org/course/course-v1:HarvardX+PH525.1x+3T2020/block-v1:HarvardX+PH525.1x+3T2020+type@sequential+block@d27d8d24b8c443c3acb9e8ac2877f99e/block-v1:HarvardX+PH525.1x+3T2020+type@vertical+block@14323fa8bd2246f78b0b51418d670ba8)
4. [CLT in Practice](https://learning.edx.org/course/course-v1:HarvardX+PH525.1x+3T2020/block-v1:HarvardX+PH525.1x+3T2020+type@sequential+block@d27d8d24b8c443c3acb9e8ac2877f99e/block-v1:HarvardX+PH525.1x+3T2020+type@vertical+block@e7d7c6baa85843fe9dc5e7518e691e5b)
5. [T-test](https://learning.edx.org/course/course-v1:HarvardX+PH525.1x+3T2020/block-v1:HarvardX+PH525.1x+3T2020+type@sequential+block@d27d8d24b8c443c3acb9e8ac2877f99e/block-v1:HarvardX+PH525.1x+3T2020+type@vertical+block@6f178bea3366401ab21d93c5d47162ea)
   * [T-test Exercises](https://learning.edx.org/course/course-v1:HarvardX+PH525.1x+3T2020/block-v1:HarvardX+PH525.1x+3T2020+type@sequential+block@997dd497b71c4232a588778f28cf894e/block-v1:HarvardX+PH525.1x+3T2020+type@vertical+block@dbafd787176f41cfabf50929f3792660)
6. [T-test in Practice](https://learning.edx.org/course/course-v1:HarvardX+PH525.1x+3T2020/block-v1:HarvardX+PH525.1x+3T2020+type@sequential+block@d27d8d24b8c443c3acb9e8ac2877f99e/block-v1:HarvardX+PH525.1x+3T2020+type@vertical+block@c5881a2f6d9047979b9c59d52ed65114)
   * [CLT and t-distribution in Practice Exercises](https://learning.edx.org/course/course-v1:HarvardX+PH525.1x+3T2020/block-v1:HarvardX+PH525.1x+3T2020+type@sequential+block@d27d8d24b8c443c3acb9e8ac2877f99e/block-v1:HarvardX+PH525.1x+3T2020+type@vertical+block@f2118c080c1f4db0984773dfb2061b0f)

## Alternative: Coursera Inferential Statistics

If you find the edX course too difficult or would like to read the same topic in alternative resources, consider [this Inferential Statistics course](https://www.coursera.org/learn/inferential-statistics-intro) offered by Duke University.

{% hint style="info" %}
Make sure to enroll in the course as before (Audit course option)
{% endhint %}

Watch the following videos:

1. [Introduction](https://www.coursera.org/learn/inferential-statistics-intro/lecture/EXe3o/introduction)
2. [Sampling Variability and CLT](https://www.coursera.org/learn/inferential-statistics-intro/lecture/lkQnZ/sampling-variability-and-clt)
3. [CLT (for the mean) examples](https://www.coursera.org/learn/inferential-statistics-intro/lecture/XhkI6/clt-for-the-mean-examples)

## Alternative: Statquest

For even more resources, check out the following videos on the topics of the normal distribution and the central limit theorem.

{% embed url="<https://www.youtube.com/watch?v=rzFX5NWojp0>" %}

{% embed url="<https://www.youtube.com/watch?v=YAlJCEDH2uY>" %}

## Congratulations!

If you made it here, then congratulations! You have successfully completed this section. Move to the next portion of the guide with the arrow buttons below.