It makes Statistics interesting. It really helps me to teach my students about the concept of normal distribution. Thank you very much!
@imartsy9 жыл бұрын
This is fun, cute, and a great explanation!
@kimberly186310 жыл бұрын
I don't get why the bimodal distribution still turns into a normal distribution for samples... Anyone?
@darkhoof6910 жыл бұрын
The samples actually will be bimodal, but the sample AVERAGES will be approximately normally distributed because the average is always in the middle of the two peaks as a measure of center. In fact, the reason the sample averages vary at all is purely because of random deviation, not because of the bimodal population. And random variation always has a normal bell curve shape.
@IvCastilla9 жыл бұрын
darkhoof69 Is a condition of the universe, same as speed of light and Fibonacci sequence.
@olganeselevska70588 жыл бұрын
For example, average of binomial distribution will be any value in the interval between 0 and 1: e.g. 0,32; 0,47; 0,78 .... Then, these averages are not distributed as taking only two possible values, and they would follow normal distribution.
@simonahuang26337 жыл бұрын
u know ,binomial distribution is the sum of n independent bernoulli distribution with the same probability of success. there is another version of central limit theorem, that is the sum of n independent variables with same expectation and variance will approximate to normal distribution when n is large. so , when n is large and p is relative small , the distribution of binomial can be approximated by normal distribution by following the central limit theorem.
@abzyfabzy60553 жыл бұрын
@@darkhoof69 so say the dragon wingspan on one peak is 20 metres and on the other it is 42 when we calculate the mean it is 62/2 which is 31, thus the peak in the centre of the curve is 31 and is shown to be one individual shaped curve, is that right?
@Hollyrocks19753 жыл бұрын
Thank you! The central limit theorem explained clearly, and cutely. :)
@khanhnguyen47394 ай бұрын
I was searching for bunny dragon hybrid babies but this is really cool too!
@Lii19425 жыл бұрын
OMG!!!! This is well-explained 😀I'll give an A+ rating for this ....... If only dynamics could be explained like this !!!😔....anyway ...thank you for this video❤
@staceytorres214711 жыл бұрын
Thank you. This is the ONLY youtube explanatory video I could understand, lol.
@elenam.42324 жыл бұрын
MAT183?
@LewianBra Жыл бұрын
If the underlying distribution is normal, the sampling distribution is normal for any sample size and doesn't become more normal with increasing sample size as claimed. This only holds if the underlying distribution is not normal. But then, in fact, no distribution in reality is perfectly normal (certainly rabbit sizes are not, as they cannot be negative). Note also that in practice the Central Limit Theorem may fail because of violations of the "identical and independent" assumption and for data quality reasons (for example with outliers).
@lisapurnawati88410 жыл бұрын
Clear explanation & entertaining. Like it!
@skabdussayeed32055 жыл бұрын
Great ! helped me a lot in my biostat class
@leakyshoes8297 Жыл бұрын
This has to be the future of learning.
@zodmorality2 жыл бұрын
This was so wonderful!
@katie11359 жыл бұрын
So cute!!!!
@charu19304 жыл бұрын
Thank you so much for such a simpler explanation
@MikaylaMurphy1304 жыл бұрын
This was super cute and easy to understand! Thank you!
@HeduAI3 жыл бұрын
Loved this!
@ilkesoetens Жыл бұрын
Amazing 🔥
@lilikasukali4 ай бұрын
my statistics prof made me watch this
@BernhardPiskernik10 жыл бұрын
great video - the only problem is: means, at least for smaller samples, have Student's t distribution an not NV.
@jerzypiano Жыл бұрын
Not quite! The t-distribution is for sample means (minus the population mean) DIVIDED BY sample standard deviations. The t-distribution arises because the sample SD is a noisy estimate of the population SD. So even if [xbar - mu] has a Normal distribution, the ratio [xbar - mu]/s has heavier tails than a Normal (more likely to get large positive or large negative values), and the t-distribution accounts for these heavier tails.
@paulhatch24336 жыл бұрын
Great video, very helpful.
@LetsTex4 жыл бұрын
I want more!!!!
@Kardash_xx7 жыл бұрын
Who else is here because they're studying Psychology at University?
@mansur_ali5 жыл бұрын
I'm studying political science & international relations, our asst. professor played this to us in class. I LOVE THOSE PİKA-BUNNİES.
@matthewcox695 жыл бұрын
@@mansur_ali I'm here for stats
@onurcanisler3 жыл бұрын
*Stats my BOIISSS*
@tyrannosaurus59723 жыл бұрын
Audiology student here🙋
@humboldt7779 жыл бұрын
Love it!
@Carooolinagarcia8 жыл бұрын
love it!
@marionm82138 жыл бұрын
This is great
@revin_john4 жыл бұрын
Who else is here because of Stats in University of Pretoria
@darxkpurrpbeats98044 жыл бұрын
STK110 man
@edutr9 жыл бұрын
very helpful for the likes of me, TY!
@grecheltaucare9012 Жыл бұрын
Is there a form to know when the sample's mean comes from a nonnormal population?
@sarahhilst64862 жыл бұрын
Hi joe
@soukkhanhsila1344 жыл бұрын
when did thee new york times start reporting on dragons as statistics?
@freelandfitness11 жыл бұрын
interesting
@ameernahas56192 жыл бұрын
Bachtel sent me
@AlessandroZir2 жыл бұрын
ok, but you left unexplained the most interesting feature of it! this video gives no insight on why &/or how we can use normal distribution to measure variables which are not normally distributed! if this is trivial, your entire argument is just tautological, and perhaps this is why you covered up its very core...