STATISTICS- What is Central Limit Theorem?

In this video we are going to understand about the Central LIMIT theorem.
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[@cherubyGreens]

Feeling amazing with Krish Naik!

[@arpitagec9]

Simplicity at its best!😊 Thank you for guiding us in our journey towards our dream☺️ Happy Teacher's Day💐

[@AnilKumar-ln1qq]

what is your dream and have you achieved it ??

[@nehasrinivas2115]

@@AnilKumar-ln1qq my dream is to friend ship with you bro

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[@unknownfacts3716]

@@nehasrinivas2115 my dream is to have friendship with you

[@zenitsu4139]

@@unknownfacts3716 my dream is to have friendship with you bro

[@p1xedge]

#point When we have the whole population, each data point is known so you are 100% sure of the measures we are calculating. When we take a sample of this population and compute a sample statistic, it is interpreted as an approximation of the population parameter. When the standard error increases, i.e. the means are more spread out, it becomes more likely that any given mean is an inaccurate representation of the true population mean. As the sample size gets closer to the true size of the population, the sample means cluster more and more around the true population mean. x-bar~u-miu (nearly equal to) when sample size increases towards population and standard error decrease.

[@DeepakKumar-uz4xy]

So it means that this theorem evaluate the population mean

[@santhanalakshmism22]

Are u a statistician or data scientist ! How to contact u ? Through mail or any if possible ..

[@nemesisamit]

@@DeepakKumar-uz4xy Not just only mean but other population parameters

[@saramohammed5421]

This is the best and simplest explanation I found for this topic!! thank you very much

[@pydj365]

what is the intuition of central limit theorem ? will it be used as statistic?

[@niveshtayal979]

Great Explanation but what is the use to this theorem and when to use this?? Please explain...

[@faresjewelry1558]

with any data set the main object is to find some pattern from. Of course, normal distribution tells us a lot about a data set, so finding or extracting a normal distribution by using the whole data set or a partition of our data set is a great advantage of finding hiding patterns. NOW, central limit theorem tells us that we can extract normal distribution even with non normal distribution data with some conditions that must be satisfied.

[@channel-lk6xz]

​@@faresjewelry1558What are those conditions

[@ThePlanetMusk]

i just started following your videos......you are very enthusiatic person Krish...

[@Songvbm]

could you please make a video regarding the differences and relations between clt and t-statistic and how t-statistic behave in an asymptotic setting?

[@tarriqyou]

Easiest explanation thank you sir

[@ranjan4495]

Found no other way to wish you... I believe... I am obliged to you as for all your support and encouragement.... On the occasion of teachers day. I bow down to you

[@GauravPadawe]

Central Limit Theorem is one of the most important concept in ML.

[@MrSmarthunky]

Can you tell me where you have used this CLT in Data Science or ML?

[@MrSmarthunky]

Can you tell me where you have used this CLT in Data Science or ML?

[@vijayanandhan4649]

​@@MrSmarthunky we might not know the shape of the distribution where our data comes from, the central limit theorem says that we can treat the sampling distribution as if it were normal.

[@hritikchoudhary8532]

In some book, it is mentioned that σ/sqrt(n) and somewhere its σ/n

[@pathikghugare9918]

if we are considering variance then σ^2 / n will come and if we are considering standard deviation then σ/sqrt(n) will come

[@andrewtumwine9711]

Krish you are the best teacher. Kindly teach us more on logs odds for upper and lower limit odds ratios. Thanks

[@rishitaahlawat2543]

thank you so much it is explained so clearly

[@sonalisingh2136]

Thanks you sir. Please create videos on distributions like chi-square distribution.

[@MalathiVinodKumar]

clear and concise

[@MadhuSudhan-nn6bd]

correct me if I'm wrong, so using clt we get the bell-shaped curve and with that, we can further do the rule of thumb ( 68-95-99%), and also please tell me what does Chebyshev's inequality do?

[@hfb9093]

Very clear explanation, thank you so much!

[@vishal56765]

thanks. You give your best. Very nicely explained

[@saketgaur185]

Thanks a lot guru ji

[@Shriyanshi_]

simply explained ..got concept on CLT thank you so much

[@vashistnarayansingh5995]

Since mean is the average value and if we plot avg value of 100 samples of size say 30 each the. We get the normal distribution hence after that if we use the 68, 95 and 99 rule of normal distribution the result will be the results of those avg right for example for we can say 68% of the mean of the of all the samples will lie between (u - 1sigma , u+1sigma ) where u is the mean of the normal distribution and sigma is the standard deviation ?

[@tharinduadikari3135]

thank you very much sir. your teaching style is great.

[@oijecnailer7977]

central limit theorem can tell us whether a sample possibly belongs to a population by looking at the sampling distribution.

[@sahayaajay7684]

Thanks for your video.Keep continue

[@sandipansarkar9211]

Very good explanation of Central Limit Theorem.Thanks

[@ishankkapoor510]

@Krish Naik - how we can take samples from a random variable as per the starting point in this video.

[@sunnysavita9071]

sir please make the video regarding hypothesis testing chi squre test and Anova

[@subbaiyanmurugesan1190]

Which book is best for central limit theorem sir?

[@sheelstera]

the last statement: ~"in a dataset most of the features will be following normal distribution except outliers"..this statement has got nothing to do with Central Limit Theorem..Central Limit Theorem is about the mean of various samples; Dataset is one of those samples

[@bhavanameravanige3253]

Thanks for the video!

[@shadiyapp5552]

Thank you sir ♥️

[@KetanChaudharyTHE-GREAT-KETAN]

I always loved the way you teach with a smiling face..... :)

[@gurdeepsinghbhatia2875]

Sir i understand that sample size should be >= 30 , but what about no. of samples ??? What should be the Number Of SAMPLES of each having atleast 30 elements (sample size)

[@HabibAlSaki]

Thanks for the video, one question, suppose S1 and S2, both have 30 samples each, now is it possible that these S1 and S2 have some common samples?

[@kantafcb1]

yes, repetition is allowed when you pick samples

[@weekendresearcher]

Will this be valid even for cauchy distribution?

[@rambaldotra2221]

Great Explanation !! Thanks a lot sir.

[@rakeshankireddy7659]

How come any number of samples mean taken from a Uniform distribution will form normal distribution? The samples mean for Uniform distribution will always be 1 for all samples. How can a normal dist. bell curve be drawn with all 1 values?

[@rishisharma5249]

Loving your content sir

[@christopherami8142]

Hi bro! where is the STATISTICS all videos playlist, i can't find them all in place in one whole playlist !!!

[@mdazharuddin3873]

Is there any limiting value for no of samples also like sample size ( n) > 30

[@gurdeepsinghbhatia2875]

Please tell me too if u get the answer

[@SreenuSreenu-v7z]

Thank you sir

[@piyushak9542]

Thank you so much! Extremely clear explaination

[@lekpjedits18]

Clean explanation. Thank you sir

[@usamajunaid2989]

And what are the conditions if the data set is distributed with poisson distribution? Do you have any article based on this proof?

[@shreyasb.s3819]

Thanks for easy explained

[@bhavya2301]

Good One...Make more visualizations...!!

[@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?

[@saswatdash1498]

What is the difference between Sample and Data points? (My assumption is data points are samples)

[@hemnathdayalan2904]

Someone correct me if im wrong "The Cumulative mean of samples obtained outside the normal distribution is equal to the mean of the actual Distribution. P.S: If I'm wrong pls explain it like a 2 mark answer

[@nitink2235]

I doubt that random variable is sampled? sampling is done for assumed distribution which may or may not be gaussian

[@letslearnjava1753]

Hello nitin , the random variables set can be sample of any population having any kind off distribution shape does not matter , if you keep on compute sample means for all fresh samples then the sample means distribution will look like gaussian, her eis a super visualization created which might give you a good feeling about this theorem-- kzbin.info/www/bejne/bJ_Ll2Z_rtJgZqs

[@Drafts_of_Archana]

Can you please provide theory along with your explanation

[@adarshvijayan9049]

awesome explanation. Better than mit edx .

[@saketedgerd8729]

Hello sir , sir in what does this therom helps us in our data set? , what can we analyse from this therom? Thank you !

[@harshchindarkar5887]

does the samples which we are considering are getting repeated ?

[@imthekaur]

Can samples taken from the population overlap while creating 100 samples from the population that you stated in the video.

[@riswanahamed6224]

if population starts overlaping then the quality and attributes of the population starts differing..same for the sample

[@qusaiahed5572]

Thank You

[@piyushthakur2649]

Thanks

[@shreyaadhvaryyu4517]

are the samples data points exclusive? is 30 an example or fixed?

[@riswanahamed6224]

sample size should be always >=34

[@madhusekhar17]

Why all distribution fallow normal distribution what is the reason behind it???

[@vishnulasya8330]

The word 'normal' in Normal distribution itself says it's mostly noticed shape in different distributions by statisticians.

[@parshuram3272]

What a easy way of explaining 🤣

[@dahoreamit]

Will S1 or S2 etc also follow a Gaussian distribution individually?

[@virendrapatil73]

No

[@sivarao7126]

How can we take a sample from a random variable ?? It is confusing for me

[@rutvikjaiswal4986]

we can used numpy library like numpy.random so that it will select random variable data .

[@mayurdhage2478]

Your videos are very good but it needs to be in order. Because the topics are scrambled everywhere and it is confusing new learners.

[@lavanyasavarala6314]

Bro...u must try to explain class in English.....most of the class in hindi..even though, it has useful content

[@Omprakash-wh4rb]

thankyou sir

[@idealmathsdeosir9308]

Good sir

[@srimantmishra2985]

Sir, I have a question. When you mentioned the sample size 'n' it was '=>30' but when you took the samples, the 'ns' are identical i.e. 30. and later it was divideded with 'sigma square' (VARIANCE). my question is what will be the value of 'n square' which is divided with the variance when 'ns' are greater than 30 but not identical ?

[@dineshpandey3096]

Thanks Sir, 👌

[@basavarajag1901]

N means ?

[@abhishekchakrabarty2930]

I have one doubt sir..if I have an element named A in sample 1,can it be possible that that element A is also included in sample 2,?

[@lalithakarthikr]

I understood the theorem but in ML where and how it will useful( in real-time scenario)? Can you please explain with an example

[@RAJATKUMAR-gl9qw]

it provide hypothetical model so if the data is normally distributed then we can easily make prediction by using domain knowledge

[@yugalsingh250]

Aaj paper CLT ki video dekh k chala jara hoon

[@brazilfootball]

Why 30?

[@saiharshareddy8808]

✨⚡

[@madarauchiha-kg4uo]

Ajj Mera exam h😢

[@charulataganguly4321]

What the hell is GD

[@sunnygoud5133]

Gaussian distribution or normal distribution

[@susratbalchhaudi3759]

puti mug