Learn the Central Limit Theorem in R: A Step by Step Guide (Updated 2023)
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. 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.
- 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.
Table of Contents
- What Is the Central Limit Theorem (CLT)?
- Central Limit Theorem Explained
- Distribution of the Variable in the Population
- Formally Defining the Central Limit Theorem
- Conditions of the Central Limit Theorem
- Significance of the Central Limit Theorem
- Practical Applications of CLT
- Assumptions Behind the Central Limit Theorem
- What Is Standard Error?
- Implementing the Central Limit Theorem in R
What Is the Central Limit Theorem (CLT)?
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 Explained
The central limit theorem in statistics states that, given a sufficiently large sample size, the distribution of the sample mean for a variable will approximate a normal distribution regardless of that variable’s in the population distribution.
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.
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
- 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)
- 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.
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:
#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.
#Calculate the population mean  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!
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.
- 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
A. This theorem states that when you take large samples from the population, the sample means will be normally distributed, even when the population is not normally distributed.
A. The three rules of the central limit theorem are as follows:
1. The data should be sampled randomly.
2. The samples should be independent of each other.
3. The sample size should be sufficiently large but not exceed 10% of the population.
A. Political/election polls are prime CLT applications. These polls estimate the percentage of people who support a particular candidate. You can refer to the above article for more.
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