The Central Limit Theorem MADE EASY [5/13]

[@barbarasheridan-ct9kk]

I haven’t looked at stats since uni which was quite some time ago. Currently trying to break into data science and re-learning stats has been my weak point. This series is fantastic in making sense of it all again.

[@andrew-jones-data-science]

Thanks Barbara! Have you seen my Data Science Infinity program?

[@Data_Mom]

Easy to understand explanation of an important concept, thanks Andrew.

[@andrew-jones-data-science]

Thanks Pooja - have you watched the whole series so far?

[@Data_Mom]

@@andrew-jones-data-science Yes, I have watched whole series. Loved the 'Statistical Distributions' one as well. Thanks for easy -to-understand way of explanation.

[@SitComCritic]

Great video! Excellent and clearly explained

[@andrew-jones-data-science]

Thanks! Have you watched the whole series? More coming soon!

[@Darwisht]

Just what i wanted 🤩

[@andrew-jones-data-science]

That’s awesome Darwish - thanks for watching!

[@scottkwiatkowski6814]

I look forward to the rest of the videos!

[@andrew-jones-data-science]

They're coming!

[@user-yz7mp4jn6o]

Hi, great video! I have a question. By the theorem, if we take a large enough amount of samples, from which we take the mean, then the mean distribution approximates the normal distribution... But, what about the size of each of those samples? Is there any theory about a "good" size for each sample? In the video you chose 40, and then 14 for the bootstramping. Could we have chosen a size of 1? :) Thank you!!!

[@kiettran1677]

explained it better than my math teacher (no hate on them tho). slay video 10/10