The Central Limit Theorem, Clearly Explained!!!

Quadruple BAM!

BAM

HMMMMMMMMMM..........

Hey Josh, can you please clarify what is 'sample size 30 rule'? Is it that we have to have at least 30 sample groups (however units in each sample groups); or in each sample groups, we have to have at least 30 units (doesn't matter how many sample groups we collect)? I am consistently confused by 'sample size' from many resources, thanks for your clarification!

The guy made the concept easy peasy lemon squeezy!!😎

Absolutely loved the way the things were elabrated.😍

oof.. this video is quite underrated... well narrated, interesting, and simple

I finally understand this after so many years! Thanks and Double BAM!

random samples correct?

Even if you're not normal, the average is normal!!! 😂

)Hi Josh, Just to clarify - say I am not repeating the experiement - I only have 10 measurements that has come from an uniform distribution - i now have mean from the 10 measurements - but I am not repeating the experiment. In this case - that single mean can be assumed as normal or if the experiment has to repeated for M times ? Reason is if I dont repeat the experiment, I only have 1 data point (mean).

Thanks a lot! I've tried the examples you gave with python. I sampled from uniform and exponential distributions, computed means and draw histograms and bam! This actually feels like magic. I'm looking forward to understand the theorem more. I read the wikipedia page and it actually seems like there are lot to learn!

Bam🤣

😂😂the theme song had me

Thank you so much

Thank you very much sir, i recently started my data analysis journey. Your videos were lot helpful

Is there any simple intuition of why the central limit theorem works?

I am a 4th Year UG at IIT Kharagpur and you will be pleased to know that almost everybody on campus loves your lectures on Probability, Statistics and Machine Learning and consider it to be the best resource for cracking company interviews. Absolutely brilliant content!

I am taking biostatistics now and I find this very helpful. However, I have one question from my textbook. It says the number of neutrophils in a sample of 2 white blood cells (binomial distribution, I suppose) is not normally distributed, but the number in a sample of 100 white blood cells is very close to being normally distributed. How does this work here? IS it similar to the central limit theorem of taking different means and plotting a histogram for the data, or is there something else in play, please?

Thanks for the crystal clear explanation Josh. BAM !!!

Very unclear, talking about “50 means” etc.