The Kolmogorov-Smirnov Goodness-of-fit Test

[@sumitchhabra2419]

This is the best explanation I have come across on KS test. I don't understand why it doesn't appear on the top of youtube algorithms search for KS test.

[@nanhl]

The example makes KS test super easy to understand! This really saves my life

[@Tweeteketje]

Thanks, extremely clear and I understand the theory much better now!

[@jadeelsa9081]

Thank you for your explanation, very clear and helpful.

[@uglyducklingkpopdata3742]

Your explanation is very clear and so goooooood. Thank you for making it!!!!

[@marianabfranca]

Thank you so much! I was looking for that since a while!!

[@EW-mb1ih]

There is a minor error in the Ft(x) value. for z=-2, it should be 0.0228 approx 0.023 and not 0.022

[@krishnavamsi9326]

So Thankful bro... You made me 2 understand in less than 5 mins

[@y4eHuk-3]

Thanks a lot! Very clearly explained!

[@LuanaSilva-rs5yd]

Thank you!

[@dmitrii5735]

The video is indeed cool! But, it does not apply to discrete distributions, doest it? In the example shown, we have exactly discrete distribution (the way it is measured).

[@fabioramilli8863]

Dear Sir, thank you for your nice video, but there are some issues that I'm missing. it's not clear why you take the D statistic and consider it as a p-value. Why do you double the p-value for a two-sided test, given that your chose the two-sided critical value of your table? Usually when a statistic exceeds a critical value, then there is a statistically significant difference. Isn't this the case? Perhaps I misunderstood, but is it possible that the right conclusion for this otherwise excellent video would be that the D statistic is equal to 0.16, and it doesn't exceed the 0.221 critical value (two-sided, alpha=0.05) thus the distribution doesn't differ statistically from the normal distribution (with mu=80 and sigma=6)?

[@johnrecker4352]

same here, i still missing the point about the double D, can someone elaborating?

[@krrsh]

Why is the D value is multiplied by 2 and considered as p-value?

[@sesppsfd3815]

Since it’s 2 tailed test

[@jeroenvermunt3372]

In a blog post I saw that you had to compare a certain statistic with the KS-value, not the p-value

[@NEILEVALAROZA]

kindly check the numbers you are using taken from the Z table. some numbers taken from 0.03 and 0.07....

[@mellisahgotora9589]

Thank u very much...been struggling to find Ft(x)

[@udiibgui2136]

What if we are not given a mean and sigma and still tasked with testing for normality?

[@edgarl.calvadoresii9479]

How is ks test different from chi square goodness of fit?

[@dc33333]

do you have to add a bonferroni correction to the calculated pvalue?

[@hosseinpiri5144]

While I like the explanation and the details you provided, I believe that last conclusion is incorrect. You are concluding that since 2D>Critical_level, then our distribution is normal, which is incorrect. Your conclusion implies that for concluding normality, it is better if D is large (i.e., two distributions have a larger difference)!

[@fatyaaaa]

So what is the right conclusion?

[@awge6666]

@@fatyaaaajust compare the D value with the critical value. Reject null hypothesis if D

[@awge6666]

@@fatyaaaabecause D value is essentially the distance (difference) between our obaerved data distribution and theoretical distribution

[@ineshi]

The tables you have been using bliz

[@notwavybaby]

This is wrong. You don't multiply the D statistic by 2. Not sure why you did this but it definitely gives the wrong answer.

[@StudyForceOnline]

The solution has been verified by a statistician working at our school

[@anmolmohanty7537]

K-S test is only for one tail test Because it is a two tailed test (≠) Not one tailed (

[@Tweeteketje]

@@anmolmohanty7537 But the table already shows at 6:26 that the column for a 97.5% one-tailed test is the same as for a 95% two-tailed test. So I also don't understand why it is doubled.