If you want to expand your knowledge in statistics, understanding how the **Central Limit Theorem** works, will be right up your street.

Before we start, you can also watch our video on the topic - just press "play" below or scroll down if you prefer reading.

First, let’s introduce a concept – a **sampling distribution**.

**Sampling Distribution**

Say you have the population of used cars in a car shop.

We want to analyze the car prices and be able to make some predictions on them. Population parameters which may be of interest are:

**mean**car price**standard deviation**of prices**covariance**- and so on.

Normally, in statistics, we would not have data on the whole population, but rather just a sample.

**Drawing a Sample**

The **mean** is $2,617.23.

Now, a problem arises from the following fact. If we take another sample, we may get a completely different **mean** - $3,201.34.

Then a third with a **mean** of $2,844.33.

As you can see, the **sample** **mean** depends on the incumbents of the sample itself. So, taking a single value is definitely suboptimal.

**Drawing Many Samples**

What we can do is draw many, many samples and create a new dataset, comprised of **sample** **means**.

These values are distributed in some way, so we have a **distribution**. When we are referring to a **distribution** formed by samples, we use the term - a **sampling distribution**. For our case, we can be even more precise – we are dealing with a **sampling distribution** of the **mean**.

**The Population Mean**

Now, if we inspect these values closely, we will realize that they are different. However, they are concentrated around a certain value. For our case - somewhere around $2,800.

Each of these **sample means** is nothing but an approximation of the **population mean**. The value they revolve around is *actually the population mean itself.* Most probably,

**of them are the**

*none***population mean**, but taken together, they give a really good idea.

In fact, if we take the average of those **sample means**, we expect to get a very precise approximation of the population mean.

**Visualizing the Distribution of the Sampling Means**

Here’s a plot of the **distribution** of the car prices.

We know that this is not a **normal distribution**. It has a **right skew** and that’s about all we can see.

**Here’s the big revelation.**

It turns out that if we visualize the **distribution** of the **sampling means**, we get something else. Something useful.

A **normal distribution**.

And that’s what the **Central Limit Theorem** states.

**The Central Limit Theorem**

The **sampling distribution** of the **mean** will approximate a **normal distribution**. No matter the **distribution** of the population - **Binomial**, **Uniform**, **Exponential** or another one.

Not only that, but its **mean** is the same as the **population mean**.

That’s something we already noticed.

**The Variance**

What about the **variance**?

Well, it depends on the size of the samples we draw but it is quite elegant. It is the **population variance** divided by the sample size.

Since the sample size is in the denominator, the bigger the sample size, the lower the **variance**. Or in other words – the closer the approximation we get. So, if you are able to draw bigger samples, your statistical results will be more accurate. Usually for the **Central Limit Theorem** to apply, we need a sample size of at least 30 observations.

**Why the Central Limit Theorem is Important**

As you probably know, the **normal distribution** has elegant statistics and an unmatched applicability in calculating confidence intervals and performing tests.

The **Central Limit Theorem** allows us to perform tests, solve problems and make inferences using the **normal distribution** even when the population is not normally distributed.

The discovery and proof of the theorem revolutionized statistics as a field and we will be relying on it a lot in the subsequent tutorials.

**Standard error**

Now, there is another important notion which spurs from the sampling distribution and is closely related to CLT. The **standard error. **It is the **standard deviation** of the **distribution** formed by the **sample means**.

In other words – the **standard deviation** of the **sampling distribution**.

So how do we find it? We know its **variance**: ** sigma** squared, divided by

**. Therefore, the**

*n***standard deviation**is

**, divided by the square root of**

*sigma***.**

*n*Like a **standard deviation**, the **standard error** shows variability. In this case, it is the variability of the **means** of the different samples we extracted.

You can guess that since the term has its own name, it is widely used and very important.

**Why We Need it**

**Standard error** is used for almost all statistical tests. This is because it is a probabilistic measure that shows how well you approximated the true **mean**.

**Important:** It decreases as the sample size increases! This makes sense, as bigger samples give a better approximation of the population.

**What’s Next**

To sum up, the **Central Limit Theorem** states:

No matter the underlying **distribution** of the data set, the **distribution** of the **sample means** would be **normal**. Moreover, its **mean** would be equal to the original **mean**. Its **variance**, however, would be equal to the original **variance** divided by the sample size.

Now, if you want to get deeper into the subject of statistics, we have covered just the right concept. Find out how estimators work and what you can use them for, in the linked tutorial.

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**Next Tutorial: **How to Use Point Estimates and Confidence Intervals