Real-world application of the Central Limit Theorem (CLT)

In this video, we talk about the real-world application of one of the most widely used theorems in data science: The Central Limit Theorem. It is the core of ‘hypothesis testing’ - an approach in statistics that lets you use data to evaluate your ideas. In fact, this theorem can be applied to a variety of real-life problems. We illustrate it with an interesting example of a business in the fish market area.

The Central Limit Theorem is a theorem in probability theory, whose first version was proposed by the French mathematician Abraham de Moivre in 1733. Moivre published an article where he used a normal distribution to approximate the distribution of the number of heads resulting from many tosses of a fair coin. The finding was nearly forgotten until the French mathematician Pierre-Simon Laplace expanded it in his monumental work in the 19th century. Over the years, numerous versions of it have been discovered and proven by other mathematicians. Fascinating, right? Watch the video to learn more!

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[theillustriousjohn]

That was an excellent explanation. The fish tank example was really good to illustrate. Keep up the good work.

[kitokid]

Interesting. please do more videos which can relate to real world applications.

[wren]

Leaving a comment just so you guys know I regularly watch your content and really very very greatly appreciate the effort you put in your videos.
Thanks a lot <3

[augustinect]

Great example, thanks for simplifying CLT

[syednawaz]

Thanks a ton.!!!! This video made the concept clear.😊

[ehatipo]

You are awesome, just wow. Thank you!!!

[nailaasghar]

Heyy ...plz tell me how can I prepare these kind of slides for my presentation kindly guide me have u did it from ppt??

[bhargavdihora]

This question was asked in GTU Data Science exam.

[akram]

What is the y axis in the curve representing exactly and why the value of the mean is represented by a higher bar than the values bigger than the mean is it only me who found this explanation more confounding 🤔

[sarataylor]

Wouldn't the standard error of this distribution be sigma/sqrt(n) and not sigma as it says in the video? Sigma is the standard deviation of the population, not the standard deviation of the distribution of sample means.

[marccepeci]

IF the underlying population distribution is NOT NORMAL, and we have samples less than 30. Let's say the samples are size
n = 5. I know the distribution of the sample means will not be normal according to the CLT. However, will the distribution have the same mean as the population mean, and will the variance be equal to the variance of the population divided by 5? Please let me know? thanks?

[quinnculver]

Great video. I really wish people would stop talking about looking up values in a statistical table though. It's 2024 for god's sake!

[evaristegalois]

An alternative solution film or take photo of the différents fish an use a computer algorithm to classify the data

[sktdebnath]

This example was great. However, I don't think you need to increase the sample size to be more precise. Well indeed you will eventually reach the population size and there will be no need of sampling. CLT on the other hand talks about the number of samples. These two are not same.

[tarun]

This explanation is unnecessarily complicating things. It was hopeless.

[ghania]

Every time I hear profit, I think of Promised Neverland. Any weebs in here that get what I'm saying?