Central limit theorem

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In this video we explore the central limit theorem. Why the Central Limit Theorem is Important? The Central Limit Theorem allows us to perform tests, solve problems and make inferences using the normal distribution even when the population is not normally distributed. The discovery and proof of the Central Limit Theorem revolutionized statistics.
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#CentralLimitTheorem #Statistics #DataScience

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[@rahulrajsingh2498]

Brief and to the point. Loved it

[@federicodragonigarcia4951]

This is video is incredible. Absolutely incredible. You made such a well done video in such a small amount of time when my many uni lectures couldn't explain anything. it just blows my mind how clear it is to me now.

[@365DataScience]

Glad you enjoyed it!

[@saurabhojha3708]

This 4 min video beats all other videos out of the park! Finally I understood. So simple, yet great. Have a like

[@anntyler6902]

Thank you for making this quick.

[@moatheshtaiwi9902]

this is the best lecture in the world

[@s.nandini12]

You sound like Dana Carvey,Love it🤗🤗

[@gauravmohan9271]

Thank you..was struggling with this concept…

[@sivarao7126]

Complete explanation in three minutes Super 🙏🏾🙏🏾

[@vstvgn4240]

does anyone notice at 1:42 each column only has 24 numbers instead of 25?

[@tenochtitilian]

1:32 Haha, that's one hell of a variance

[@KhalidAli-po2yu]

Thank you for this informative explanation. I have a question thought, at 1:10 you said the distribution of the means of the random samples will be as same as the distribution of the original dataset! What if the original dataset is skewed or uniform or anything rather than a normally distributed dataset? Does this mean that the distribution of the means of random samples will be also skewed? Isn't that the opposite of what the theorem state?

[@AdamSandler-y4i]

No he didnt say that. Listen again.

[@ApexPlays777]

Thank you 🙏🏽

[@piyalikarmakar5979]

Sir, for getting Normal Distribution, need to increase number of samples drawn from population or the size of the samples?

[@BorisNVM]

clear and simple

[@azrflourish9032]

Great video as always!!! Thank you!!

[@AkshayKumar-kj3wp]

Does this also assume that the sample data can be normally distributed? Since always we will be dealing with Sample data?

[@dharmendrakartikey6987]

Very informative video

[@jheel-patel]

What is the use of Central Limit theorem in machine learning? How and why should i use it on the datasets?

[@nxbil2397]

Wy

[@Omsy828]

Great video, please organize the follow-up video in playlist so we can watch next!

[@billjensen51]

well you certainly said words

[@abhimanyukarkara4218]

Could you please explain how come the variance is so big? 82k in the population? When all the numbers are lower than 1000

[@Oceanray7629]

thank u, so hellpful

[@fhaiza1543]

Normal uniform exponential binomial

[@TaylorQuince19493]

I understand that the sample size matter (ex.more than 25). But does the number of random matter? Would it work if it’s less than 30 which is presented?

[@OfficialRapMV]

It's the minimum amount for it to be graphically presented as normally distributed. Too little and you will not see it clearly, yet the theorem still applies, just that it won't be as convincing as a sample size bigger than 30.

[@pratiklavhale5541]

Thank you very much

[@marccepeci2980]

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?

[@daniel_diaz97]

Thank you

[@Yu1551]

I don’t even like stats but I enjoyed this video

[@365DataScience]

So happy to hear it! Thank you!

[@moatheshtaiwi9902]

you should replace the lecture in udemy course with this wonderful lecture or add it the course

[@ljljhg123]

I though total sample size n is 30? 25 is just the number Of observation in each sample?

[@asdfgkelly7806]

true. I thought it's 30.

[@shashibhushankr16]

Thank you for such a nice so this video

[@365DataScience]

We're happy you liked it!

[@voxelsama]

I liked this one way better than Khan Academy's explanation

[@BhavyaExplores]

Super Duper!

[@oksana8695]

Who would dislike this video?

[@MisterBinx]

Can professors stop trying to do their own awful videos and just link us to the simple explanations. Why should we waste hours when something can be explained in 4 minutes?

[@malaika6441]

Your voice sounds like a 25 year old Donald Trump

[@62294838]

I guess one thing the Academy should mention is k. If n is at least 25 so what about k? The true N here should be n times k in the video. It confuses people. Poor work.

[@emreozgun3846]

makes more sense, thanks

[@zhenminliu]

This video is oversimplistic.

[@axelsanchez5849]

It’s perfect for an introduction for the subject, stfu