If H0 is true and we reject it, that is called type-1 error, if H0 is false and we failed to reject it, that is called type-2 error.

Please keep posting all the data science tuts 👌👌👍👍👍

Hi Krish, the statement at @6:38 actually needs your attention, in Hypothesis testing we either reject or fail to reject null hypothesis. Hence, in case of lack of evidence we will fail to reject null hypothesis and cannot conclude anything. In lines to the judicial example that you have taken, the null hypothesis is that the person is innocent and it is upon the onus of investigating body to prove that the person is guilty, in case they don't have proper evidence to prove that the court/justice system fails to reject the null hypothesis of person being innocent.

0:26 Sir, please discuss Friedman test and Nemenyi test for non parametric test

Sir I really like your videos and follow you regularly. I would really love to see you upgrade your audio quality as you hit 100K subs !!

Your videos are of high energy and trigger the concept.

Excellent description Krish.... Keep it up ... I'm waiting for your next videos on same topics. Thanks

Thank you sir,
Good start of the day with hypothesis testing.

thanks for this videos sir great explanation waiting for more stuff on Statistics..looking forward for test of mean and test of variance all the test used in them and ANOVA and 2 way ANOVA...

Struggled to understand this concept since long
Thank you so much 🙏

crystal clear explanation.:elbowcough::hands-yellow-heart-red:
Thank you.

Best Data Science Guru 👍

Nice Video Sir , thanks for your efforts .

Thanks a lot Sir!
Now I can understand Hypothesis Testing much better :)

Great explanation Krish.Thanks

at 9:25 krish went to rap mode with his hypothesis testing. confusios matrix has confused him too.😂

Hi sir
I guess the example mentioned in the YouTube video is wrong regarding the stock going to crash or not.
H0 : The stock is going to crash
H1 : The stock is not going to crash
According to the confusion matrix, the top of the table is the actual values(H0=True/H1=True) and the left side is predicted values(H0=True/H1=True).  I don't understand why you have added, do not reject and reject in the left.
In the mentioned example,
Type 1 error occurs when H1 is predicted to be True, but H0 is True.
It is the case when predicted that the market is not going to crash, actually it does crash.
Type 2 error occurs when H0 is predicted to be True, but H1 is True.
It is the case when predicted that the market is going to crash, but actually it doesn't crash.

Thank you so much sir for clearing basic concept

Thank you sir for the lovely explanation, Please make a video of Practical  implementation of Z Test in Python , I did not found any video on z test implementation, Your T-Test video is soo good make one video on "Z Test" as well if there is the video already please share the link in the comment.

Thank you for being so lucid. What you are doing is amazing work. God bless you. It is because of people like you, this planet is worth.