Central Limit Theorem: Formula, Definition & Significance

Introduction

What is one of the most important and core concepts of statistics that enables us to do predictive modeling, and yet it often confuses aspiring data scientists? Yes, I’m talking about the central limit theorem (CLT). It is a powerful statistical concept that every data scientist MUST know. Now, why is that?

Well, the central limit theorem (CLT) is at the heart of hypothesis testing – a critical component of the data science and machine learning lifecycle. That’s right, the idea that lets us explore the vast possibilities of the data we are given springs from CLT. It’s actually a simple notion to understand, yet most data scientists flounder at this question during interviews.

In this beginner’s tutorial, we will understand the concept of the Central Limit Theorem (CLT) in this article. We’ll see why it’s important and where it’s used, and learn how to apply it in R and python.

Learning Objectives

• In this tutorial, we will learn about the Central limit theorem and conditions of the central limit theorem.
• We will also learn about the Central limit theorem assumptions, significance, and implementation in R language.

What is Central Limit Theorem?

The central limit theorem states that when the sample size is large, the distribution of the sample mean will be normal. This holds true regardless of the original distribution of the population, be it normal, Poisson, binomial, or any other type.

Unpacking the meaning of that complex definition can be difficult. That’s the topic of this post! I’ll walk you through the various aspects of the central limit theorem (CLT) definition and show you why it is vital in statistics.

Measure of Central Tendency

The measure of central tendency (central location/measures of center) is the summary measure that tries to explain the whole set of data with a single value that represents the middle or center of a distribution.

Understanding CLT with Example

Let’s understand the central limit theorem with the help of an example. This will help you intuitively grasp how CLT works underneath.

Consider that there are 15 sections in the science department of a university, and each section hosts around 100 students. Our task is to calculate the average weight of students in the science department. Sounds simple, right?

The approach I get from aspiring data scientists is to simply calculate the average:

• First, measure the weights of all the students in the science department.
• Add all the weights.
• Finally, divide the total sum of weights by the total number of students to get the average.

But what if the size of the data is humongous? Does this approach make sense? Not really – measuring the weight of all the students will be a very tiresome and long process. So, what can we do instead? Let’s look at an alternate approach.

• First, draw groups of students at random from the class. We will call this a sample. We’ll draw multiple samples, each consisting of 30 students.

• Now, calculate the individual mean of these samples.
• Then, calculate the mean of these sample means.
• This value will give us the approximate mean weight of the students in the science department.
• Additionally, the histogram of the sample mean weights of students will resemble a bell curve (or normal distribution).

Central Limit Theorem Formula

The shape of the sampling distribution of the mean can be determined without repeatedly sampling a population. The parameters are based on the population:

Distribution of the Variable in the Population

Part of the definition for the central limit theorem states, “regardless of the variable’s distribution in the population.” This part is easy! In a population, the values of a variable can follow different probability distributions. These distributions can range from normal, left-skewed, right-skewed, and uniform, among others.

• Normal: It is also known as the Gaussian distribution. It is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean.
• Right-Skewed: It is also known as the positively skewed. Most of the data lie to the right/positive side of the graph peak.
• Left-Skewed: Most of the data lies on the left side of the graph at its peak than on its right.
• Uniform: It is a condition when the data is equally distributed across the graph.
• This part of the definition refers to the distribution of the variable’s values in the population from which you draw a random sample.

The central limit theorem applies to almost all types of probability distributions, but there are exceptions. For example, the population must have a finite variance. That restriction rules out the Cauchy distribution because it has an infinite variance.

Additionally, the central limit theorem applies to independent, identically distributed variables. In other words, the value of one observation does not depend on the value of another observation. And the distribution of that variable must remain constant across all measurements.

Formally Defining the Central Limit Theorem

Let’s put a formal definition to CLT:

Given a dataset with unknown distribution (it could be uniform, binomial or completely random), the sample means will approximate the normal distribution.

These samples should be sufficient in size. The distribution of sample means, calculated from repeated sampling, will tend to normality as the size of your samples gets larger.

The central limit theorem has a wide variety of applications in many fields and can be used with python and its libraries like numpy, pandas, and matplotlib. Let us look at them in the next section.

Conditions of the Central Limit Theorem

The central limit theorem states that the sampling distribution of the mean will always follow a normal distribution under the following conditions:

• The sample size is sufficiently large. This condition is usually met if the size of the sample is n ≥ 30.
• The samples are independent and identically distributed, i.e., random variables. The sampling should be random.
• The population’s distribution has a finite variance. The central limit theorem doesn’t apply to distributions with infinite variance.

Significance of the Central Limit Theorem

The central limit theorem has both, statistical significance as well as practical applications. Isn’t that the sweet spot we aim for when we’re learning a new concept? As a data scientist, you should be able to deeply understand this theorem. You should be able to explain it and understand why it’s so important. Criteria for it to be valid and the details about the statistical inferences that can be made from it. We’ll look at both aspects to gauge where we can use them.

Statistical Significance of CLT

Analyzing data involves statistical methods like hypothesis testing and constructing confidence intervals. These methods assume that the population is normally distributed. In the case of unknown or non-normal distributions, we treat the sampling distribution as normal according to the central limit theorem.

If we increase the samples drawn from the population, the standard deviation of sample means will decrease. This helps us estimate the mean of the population much more accurately. Also, the sample mean can be used to create the range of values known as a confidence interval (that is likely to consist of the population mean).

Practical Applications of CLT

The central limit theorem has many applications in different fields.

Political/election polls are prime CLT applications. These polls estimate the percentage of people who support a particular candidate. You might have seen these results on news channels that come with confidence intervals. The central limit theorem helps calculate the same.

Confidence interval, an application of CLT, is used to calculate the mean family income for a particular region.

Assumptions Behind the Central Limit Theorem

Before we dive into the implementation of the central limit theorem, it’s important to understand the assumptions behind this technique:

• The data must follow the randomization condition. It must be sampled randomly
• Samples should be independent of each other. One sample should not influence the other samples
• Sample size should be not more than 10% of the population when sampling is done without replacement
• The sample size should be sufficiently large. Now, how will we figure out how large this size should be? Well, it depends on the population. When the population is skewed or asymmetric, the sample size should be large. If the population is symmetric, then we can draw small samples as well.

In general, a sample size of 30 is considered sufficient when the population is symmetric.

The mean of the sample means is denoted as:

µ _X̄ = µ

where,

• µ _X̄ = Mean of the sample means
• µ= Population mean

And the standard deviation of the sample mean is denoted as:

σ _X̄ = σ/sqrt(n)

where,

• σ _X̄ = Standard deviation of the sample mean
• σ = Standard deviation of the population
• n = sample size

And that’s it for the concept behind the central limit theorem. Time to fire up RStudio and dig into CLT’s implementation!

The central limit theorem has important implications in applied machine learning. This theorem does inform the solution to linear algorithms such as linear regression, but not for complex models like artificial neural networks(deep learning) because they are solved using numerical optimization methods.

What Is Standard Error?

It is also an important term that spurs from the sampling distribution, and it closely resembles the Central limit theorem. The standard error. The SD of the distribution is formed by sample means.

Standard error is used for almost all statistical tests. This is because it is a probabilistic measure that shows how well you approximated the truth. It decreases when the sample size increases. The bigger the samples, the better the approximation of the population.

Implementing the Central Limit Theorem in R

Are you excited to see how we can code the central limit theorem in R? Let’s dig in then.

Understanding the Problem Statement

A pipe manufacturing organization produces different kinds of pipes. We are given the monthly data of the wall thickness of certain types of pipes. You can download the data here.

The organization wants to analyze the data by performing hypothesis testing and constructing confidence intervals to implement some strategies in the future. The challenge is that the distribution of the data is not normal.

Note: This analysis works on a few assumptions and one of them is that the data should be normally distributed.

Solution Methodology

The central limit theorem will help us get around the problem of this data where the population is not normal. Therefore, we will simulate the CLT on the given dataset in R step-by-step. So, let’s get started.

First, import the CSV file in R and then validate the data for correctness:

Output:

#Count of Rows and columns
9000 1

#View top 10 rows of the dataset
   Wall.Thickness
1        12.35487
2        12.61742
3        12.36972
4        13.22335
5        13.15919
6        12.67549
7        12.36131
8        12.44468
9        12.62977
10       12.90381

#View last 10 rows of the dataset
     Wall.Thickness
8991       12.65444
8992       12.80744
8993       12.93295
8994       12.33271
8995       12.43856
8996       12.99532
8997       13.06003
8998       12.79500
8999       12.77742
9000       13.01416

Next, calculate the population mean and plot all the observations of the data.

Output:

#Calculate the population mean
[1] 12.80205

See the red vertical line above? That’s the population mean. We can also see from the above plot that the population is not normal, right? Therefore, we need to draw sufficient samples of different sizes and compute their means (known as sample means). We will then plot those sample means to get a normal distribution.

In our example, we will draw m sample of size n sufficient samples of size 10, calculate their means, and plot them in R. I know that the minimum sample size taken should be 30, but let’s just see what happens when we draw 10:

Now, we know that we’ll get a very nice bell-shaped curve as the sample sizes increase. Let us now increase our sample size and see what we get:

Here, we get a good bell-shaped curve, and the sampling distribution approaches the normal distribution as the sample sizes increase. Therefore, we can consider the sampling distributions as normal, and the pipe manufacturing organization can use these distributions for further analysis.

You can also play around by taking different sample sizes and drawing a different number of samples. Let me know how it works out for you!

Conclusion

The central limit theorem is quite an important concept in statistics and, consequently, data science, which also helps in understanding other properties such as skewness and kurtosis. I cannot stress enough how critical it is to brush up on your statistics knowledge before getting into data science or even sitting for a data science interview.

I recommend taking the Introduction to Data Science course – it’s a comprehensive look at statistics before introducing data science.

Key Takeaways

• The central limit theorem says that the sampling distribution of the mean will always be normally distributed until the sample size is large enough.
• Sampling should be random. The samples should not relate to one another. One sample shouldn’t affect the others.

Frequently Asked Questions?