Skewness and Kurtosis : the two summary stats they never taught you

[@OwenMcKinley]

Kurtosis is critically important and it's not typically discussed in entry- or even intermediate-level statistics courses. So happy to see you covering it here, your content deserves an Olympic gold medal. Thank you so much

[@ritvikmath]

thanks for the words! Kurtosis is one of my favorite topics haha

[@JJ_TheGreat]

​@ritvikmath Yes, I ❤️ that word! It sounds like a disease... I am going to stay home today because I have the kurtosis! 😉

[@jameslavin3742]

As someone who is currently transitioning into Data Science from an Econ background, I appreciate the intuitive examples you use in your videos! Thanks and keep up the great work!

[@Luis_Rum]

+1

[@ritvikmath]

Glad it was helpful!

[@ejigumulatu5435]

After 10 years of education, I realized you are a super statistician and teacher!!!! Thanks

[@nikitap6368]

Just started learning about kurtosis for a finance course and am very happy to have found your video! Very helpful. I love how you break things down so that they're easy and intuitive to understand.

[@ritvikmath]

Glad it was helpful!

[@aneeq09]

which course

[@vasusarraf9316]

Thank you for explaining the topic in such a simple manner, this topic initially was quite difficult. Thanks!

[@sophiestaunton88]

I just started studying statistics and this video was really useful and accessible, thank you!

[@JJ_TheGreat]

I first learned about kurtosis in my high school research class - it was a stat we looked at for our project, but I really didn't know what it was aside from being a weird word... Thank you for the explanation. This is a great, well-needed video!

[@Nxck2440]

9:52 correction: kurtosis of a standard normal distribution is 3. the excess kurtosis is then whatever the kurtosis is minus 3.

[@eskausimon]

Lucky to have bumped on this video. Learnt a lot from it. Thanks for your approach. It's true most lectures avoid these.

[@peterwestfall6924]

Very good. A slight clarification though: heavy tails does not necessarily mean "a lot of outliers." A single observation that is 7 standard deviations from the mean is enough to indicate a heavy tail. After all, such an observation would not happen, for all intents and purposes, under normality. The tails of heavy tailed distributions, while much higher than normal tails, are still very close to zero.

[@fidelemmanuel5989]

I have a bachelor's and Masters degree in statistics and I can say this is the best explanation so far on these concepts

[@matteogirelli1023]

Very useful especially the example the application of kurtosis and skewness on growth and inequality

[@ritvikmath]

Thanks!

[@alireza1702]

I learned more than any of my uni courses in this video Thank you so so much

[@LuizaDreamsDeutsch]

Hi, thank you sooo much for the explanation! You really brought out the most important characteristics and meaning of each metrics. :)

[@ritvikmath]

Glad it was helpful!

[@kevinkasp]

Thank you. Well done job of fleshing out the topics.

[@subahzareenkhanam7125]

My man- u r my savior and a legend.❤

[@chrislin5938]

Your videos are really amazing. Thank you so much for the intuitive and motivating examples. They are true gold.

[@hongjiang3399]

I really want to know more about Kurtosis! Please make a video about it!😊

[@ritvikmath]

Thanks for the suggestion!

[@RaviSankarKerla]

very well explained , clear and Crisp

[@alg0rithm1]

I have a BA in Stats and can confirm the accuracy of the video's title.

[@sandeepvk]

Skewness measures the degree of asymmetry of the distribution, while Kurtosis measures the degree of peakedness and flatness of a distribution

[@peterwestfall6924]

Kurtosis does not measure peakedness or flatness. Perfectly flat-topped distributions can have very high kurtosis, and infinitely peaked distributions can have very low kurtosis. Examples of such distributions are given on the current Wikipedia page.

[@thedislikebutton163]

I never understood moments of a distribution. 12:21 hit me like a brick when I realised.

[@ritvikmath]

It’s quite a strange concept unless properly motivated

[@dickheadrecs]

same here - there’s a clarity to the way this is outlined here that is just crystalline. subbed

[@Geza_Molnar_]

"Look at full distr." + "plot that full distribution and think about..." The best descriptor of a set is the set :-)

[@ritvikmath]

Pretty much!

[@sbulelenqophiso7239]

Great example sir , I Understood

[@JJ_TheGreat]

11:29 What about the median? Which is better - mean or median? Could you do a video on this? Thanks!

[@pabloagogo1]

This is really great!

[@tahirhanif9669]

Excellent summary, thank you

[@ProfMark]

Awesome explanation. Bravo!

[@ritvikmath]

Many thanks!

[@Hobbies_forkids]

Thank you for this viseo. For some reason never thought about including kurtosis in my descriptive statistics analysis. Do you think you can do (or maybe you already did) vidoe on advanced statistics analysis: cdf, etc. More from the perspective what else can be added to it. Thank you!

[@woowooNeedsFaith]

Problem with these higher moments is with their sensitivities to outliers. How much more data we need to "reliably" estimate these (ever-increasing) higher moments?

[@ritvikmath]

That is an interesting angle to look at it!

[@andica4995]

9:51 A Normal distribution has a kurtosis of 3 not zero. Excess kurtosis for normal distribution is zero. Pls correct me if I'm wrong.

[@ritvikmath]

yes you are right. I missed the word "excess" there.

[@navinsengundhar]

Best explanation on this topic :)

[@ResilientFighter]

Very much needed. Thank you

[@JJ_TheGreat]

12:54 Wow, I didn't know about moments - except for "moment-generating functions".

[@ridwanwase7444]

Can you explain how to.calculate skewness and kurtosis?

[@tanaykamath1415]

Loved this video!

[@DanielJanzon]

I think the full distribution in visual form is the best for non-technical audiences. Then you don't have to dumb down and you don't have to explain it much either. If graphs are forbidden for some odd reason, maybe "buckets" can be a good way. Like 20% of the students had less than 30p, 30% in range 30-50p etc. Honestly even standard deviation is tricky to understand even for technical people. Ask someone who "should know" to explain - on the spot - the difference between standard deviation and the mean deviation.

[@JellisVaes]

Great video! Thank you.

[@matteogirelli1023]

Can you do a similar video with examples on other moments and things like statistical entropy? 😁

[@ritvikmath]

Great suggestion!

[@JJ_TheGreat]

9:16 But you said that the standard deviations of both distributions is the same, correct? How is that possible that the number of outliers in both distribution differs, YET they still both have the same standard deviation? Thanks!

[@jijie133]

Thank you for you video.

[@ritvikmath]

You are welcome

[@DW-xt7vz]

Very well done

[@ridwanwase7444]

Can you Please explain how to measure skewness and kurtosis numerically? 👏❤️

[@ald385]

You are the best!

[@ritvikmath]

Thanks!

[@typicalgamer5560]

Cheers pal!

[@shofada]

👏 👏 👏 Very well done!

[@ritvikmath]

Thank you! Cheers!

[@japhetbaniqued5347]

What are the meaning of those symbols in the formula

[@Phil-oy2mr]

Thank you for the explanation! Can you further clarify the difference between standard deviation and kurtosis? They feel similar as they relate to outliers to some degree, at least when you explained it in your example. Thanks!

[@LaFacedera]

Kurtosis puts more weight to observations that are far away from the mean.

[@ATutuse]

CFA level 1 brought me here

[@AshishRanjan-jn7re]

kurtosis of normal distribution is 3. Excess kurtosis is amount exceeding 3.

[@nihilisticalbino]

Thankss!

[@ReuelRamos]

One thing that kills me is when, like in the video example, the blue distribution is skewed to the left, my brain wants to understand as the other way around, e.g, 'to the right', because that is what the figure looks like, as if it was 'pending to the right'. D:

[@Set_Get]

THANK YOU, in my case for describing Kurtosis.

[@ritvikmath]

Of course!

[@farazinbesat3890]

Good old pen and paper.

[@RahulSingh-up8jo]

Wow!

[@veeru-2023]

Congratulations ♥, You got a new Subscriber!. @ritvikmath, You explained it in very intuitive way.