Real-world application of the Central Limit Theorem (CLT)
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- Опубліковано 30 вер 2024
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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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This explanation is unnecessarily complicating things. It was hopeless.
Interesting. please do more videos which can relate to real world applications.
Glad you like it! For similar videos with real-world examples you can check out our 365 Data Use Case series: ua-cam.com/play/PLaFfQroTgZnzJn7VXKLuiZXPF1y5Wzflj.html
Garcia Mark Thompson Betty Young Jeffrey
Davis Richard Gonzalez Scott Rodriguez George
Every time I hear profit, I think of Promised Neverland. Any weebs in here that get what I'm saying?
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
Thank you, this means a lot! We are very happy that you enjoy our content!
Martin Ruth White Michael Garcia Kevin
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.
I believe they meant the standard deviation of the sample mean distribution (aka standard error directly). They didn't explain on how you obtain that standard error which is exactly what you stated (σ/sqrt(n)).
That was an excellent explanation. The fish tank example was really good to illustrate. Keep up the good work.
Bayer Parkways
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 🤔
Great example, thanks for simplifying CLT
Jacobson Vista
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!
And FYI, at 5:47 it should say should say "mean of sample means of 48" not "sample mean of 48"; and then maybe "standard deviation of sample means" for clarity.
Heyy ...plz tell me how can I prepare these kind of slides for my presentation kindly guide me have u did it from ppt??
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?
no and no, gotta either have a normally distributed population or n>30 for those conditions to apply (and it would be divided by the square root of the sample size, not the sample size itself)
@@VaiskHD
Are you sure? I thought no matter how the population is distributed if you take large samples for a population the sample means will be normally distributed
An alternative solution film or take photo of the différents fish an use a computer algorithm to classify the data
😢😂
Thanks a ton.!!!! This video made the concept clear.😊
You are awesome, just wow. Thank you!!!
This question was asked in GTU Data Science exam.
Eh, not a great example problem.
this kind of example helps a lot
excellent
Thanks!
Great example! Thank you.
context is dated.
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.
Waao
interesting
wassup 11 - ITALIAE!!!!!!!!!!!!!!!
This is one of the best videos explaining CLT in basic language that is easily understandable. I teach intro to stats to undergrads and my students come from all different math backgrounds. All the other CLT videos I've found use language that is more difficult to grasp for students. This video breaks it down without stat jargon that loses students' attention. THANK YOU. I'm showing them this video.