If we talk about the central limit theorem meaning, it means that the mean value of all the samples of a given population is the same as the mean of the population in approximate measures, if the sample size of the population is fairly large and has a finite variation. The central limit theorem is one of the important topics when it comes to statistics. In this article, we will be learning about the central limit theorem standard deviation, the central limit theorem probability, its definition, formula, and examples.
Central Limit Theorem Definition
Let us first define the central limit theorem.
The Central Limit Theorem states that the overall distribution of a given sample mean is approximately the same as the normal distribution when the sample size gets bigger and we assume that all the samples are similar to each other, irrespective of the shape of the total population distribution.
Central Limit Theorem Statistics Example
To understand the Central Limit Theorem better, let us consider the following example.
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Assume that you have 10 different sports teams in your school and each team consists of 100 students. Now, we need to find out the average height of all these students across all the teams. How will we do it when there are so many teams and so many students?
Well, the easiest way in which we can find the average height of all students is by determining the average of all their heights. To do so, we will first need to determine the height of each student and then add them all. Then, we will need to divide the total sum of the heights by the total number of the students and we will get the average height of the students. Well, this method to determine the average is too tedious and involves tiresome calculations. So, how do we calculate the average height of the students? We can do so by using the Central Limit Theorem for making the calculations easy.
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In this method of calculating the average, we will first pick the students randomly from different teams and determine a sample. Every sample would consist of 20 students. Then, we would follow the steps mentioned below:
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First, we will take all the samples and determine the mean of each sample individually.
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Then, we will determine the mean of these sample means.
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This way, we can get the approximate mean height of all the students who are a part of the sports teams.
If we find the histogram of all these sample mean heights, we will obtain a bell-shaped curve.
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Note: It is important to remember that the samples that are taken should be enough by size. When we take a larger sample size, the sample mean distribution becomes normal when we calculate it by repeated sampling.
Central Limit Theorem Formula
Now that we learned how to explain the central limit theorem and saw the example, let us take a look at what is the formula of the Central Limit Theorem.
We can apply the Central Limit Theorem for larger sample size, i.e., when n ≥ 30.
The formula of the Central Limit Theorem is given below.
μx = μ
𝜎x= 𝜎/√n
Here,
μ is the population mean
𝜎 is the standard deviation of the population
μx is the sample mean
𝜎x is the sample standard deviation
n is the sample size