One of the first things taught in introductory statistics textbooks is that correlation is not causation. It is also one of the first things forgotten.

Thomas Sowell

The thing about statistical concepts is that beyond a point of learning where you have learned and codified all knowledge with understanding, there is a need to maintain a compendium. **Thereby, it’s a good way to remember all the tools available and not just the hammers (read Deep Learning)**. The source code is in the Title itself.

## Central Limit Theorem

The central limit theorem states that if you sample a sufficient number of samples from a distribution, **albeit any kind of distribution and take their means**. This process when repeated over multiple iterations, the **mean value will follow a normal distribution.**

The below histogram shows means from a normal distribution

The below histogram shows the mean values generated from a **Poisson Distribution** with the same mean as an above-normal distribution. **A Poisson Distribution** has the same standard deviation as the mean value.

The question then becomes, what’s the significance of knowing the Central Limit Theorem?

**Different Movements**

- Kurtosis
- Skewness
- Mean

And so on

To be continued …