Tutorial 32- All About P Value,T test,Chi Square Test, Anova Test and When to Use What? 

Amazing, its like my 5-6 hour online class video merged into a 12 minute video.

Amazing video sir... It has cleared my doubt on one of contradictory topic. Thank you very much for this teaching........

This was a good overview of the different hypothesis tests. Looking forward to seeing more videos from you in this series. 😊

Thank you so much Sir...now i learned and understand the difference in between the T test, correlation, ANOVA.. P value significance ...etc

Super and Great, This was what I was waiting for long time, Thank you again 🙏

best channel for learning statistics i've found so far. Great job

You're doing a great job, sir. Understanding these concepts is as important as knowing how to code.

It's the best overview of tests I have seen on RU-vid. Awesome dear sir.... Thank you.

Good job !! Some parts of the explanation can be improved, especially your point about ANOVA test when a categorical variable has more than 2 possible values. Consider slowly down and collecting your thoughts together and your videos will be even more effective.

Thank you so much for putting it all together in this concise video.

Watching you hustle...i push my limits 🙏 Thanks you so much Sir.

Great explanation, much better than the education I received in the last three months combined.

Wonderful explanation, thank you very much for making it so easy and interesting

I am grateful for the brief information for the various test in the hypo & null hypo. helpful

Thank you so much. I love your method and pace of teaching.

Typically you reject Null Hypothesis or You Fail to reject Null Hypothesis. "Accepting" H0 or Ha term is typically not used..

sir! really blessed to watch your videos!! ur passion towords it make me feel enlightned 💯🙏

You sir are amazing! Thank you for this video!

thank you for doing this video. it is a very useful and good explanation with a simple example.

Its great to seea good video on hypothesis testing.... good going..

Super krish naik jeee crystal clear explanation …..preparing for PhD It’s helping me a lot thank you once again

THIS IS YOUR BEST VIDEO SO FAR !

This video clear 80% of our Hypothesis testing concepts. It's a very good explanation.

thank you so much!! you make things easier!!

Amazing this has given me a clear understanding.

I think the starting point of Data Science is the Analysis of Data and these tests determine the Algorithm and the Regularization method to implement to minimize the cost function (RSS). Read recently that 1) Co-variance and Multi-Collinearity would have impact on the Coefficients and NO impact on predictions 2) There are L1 and L2 Norm regularization methods. A study (Mark Schmidt CS542B Project Report December 2005) says that L1 with Optimizing Least Squares is better than L2. Reason being that L2 does not address Parsimony (sparsity) of the model and Interpretability of the coefficients values and all it aims is Shrinking the Coefficients. L1 regularization has many benefits of the L2 and yet, sparsity and interpreting coefficients is easy. While above two are understandable in English but not as Statistics. May I request you to cover these, if possible, in your next session. Its so nice to see "whys" and "whens" in this video, which I think is the matter for Data Scientist. Great Work Krish. Please keep it going with more Whys and Whens.

best playlist i have seen ever

Excellent video, describe concept clearly

Thank You, very clear explanation

Thanks Krishh for the awesome video.

Thanks for the lucid explanation.

Easy to understand.. You have enlightened me :D

The p-value is the likelihood of the observed data, given that the null hypothesis is true. The more it is low, the more we are confident to reject H0

Very good explaining sir. Thank u ❤

Very well explained Krish

Great video👍👍 really helpful

thanks alot for this beautiful content

Well explained! Thanks

Thanks this helps!!

Good job!! I really like and understand your video.

Great video!

Best explanation.. 👍👍

very nicely explained. Thank you

Very good video again as earlier. The way of connecting different concepts together is the difficult part for beginners and students. Your approach to answering the above issues are excellent Krish. Thank you very much. Please continue your good job for this world.

Thank u so much sir it really helped me a lot to understand this concept

Thank You sir... It was very knowledge full

watching the video for second time for revision. Thanks

Hi Krish, thanks for this amazing video. Could you explain this using python with the sample data set.

Excellent tutorial

superb well explained appreciated

Your explanation creating interest to learn statistics

Thanks for the compacted video & all the tests at one place. I don't think so there is any other video on you tube explaining all the tests in such short & meaningful way. Nice video. Also, just got a doubt what test do we need when there is a categorical & numeric variable combination?

Thank you very much, Krish. Tomorrow I have a mock interview on Machine Learning. a lot of thanks to you.

I have watch more than 5 videos and still could not understand and finally sir videos has made it so comprehensible.....watching this video just 2 hrs before my exam😂

Excellent Teaching. Thanks

Awesome explanation thank you

Sir you explained it very well, in a very easy to understand way. The only problem was audio quality. Else everything was perfect.

Simply superb

Excellent teaching

Thanks a lot. Thanks for excellent explaination

Thank you so much for this nice explanation

Excellent Class Sir ....

as always awesome

You need a correction: Rejecting the null hypothesis does not mean that we accept the alternate hypothesis. We never accept the alternate hypothesis. We only reject the numm hypothesis or fail to reject. We don't do anything with the alternate hypothesis.

You speak very fast! thank you for explaining so well

Very nicely explained

p-value Given a chance model that embodies the null hypothesis, the p-value is the probability of obtaining results as unusual or extreme as the observed results. Alpha The probability threshold of “unusualness” that chance results must surpass for actual outcomes to be deemed statistically significant.

very clearly explained..

Helpful explanation.

super and great video. it's powerful for me

wonderful explanation

God Bless you Sir ...

Q- why we use P=0.05 or 5%? A- From experience or we can say from previous experiments we have concluded that from a population about 5% outcome is defective or we can say we have to reject that amount of data that falls within or equal to 5%.

Hello, Can you please add a video implementing the pipelining technique for ensembling more than two different algorithms together.

Oh my god Krish got angry 7:02😂😂😂,jokes apart you are gr8 teacher.

Thank you for your effort sire

We can only reject null hypothesis but never accept alternate hypothesis. Based on test we can only conclude that we either have evidence in favor of null hypothesis or not.

Thank you buddy

Excellent ... please upload more videos

Hi Krish, Great video. Thank you very much. Can you please do a video on Z-test vs T-test?

Very nice explanation. Linked to these types of tests, when do we use the F-test?

You teach awesome sir

Impressive !

Crisp and to the point. Good one Krish.

this guy came 4 years too late for me! thanks for this

One category Feature --> One sample proportional test or Z test Two Category Features --> Chi-Squared Test One Continoues feature --> T -test Two continoues variable --> Co-realtion plus t test numberical plus category variables--> annova

big thanks Krish!

Very helpful thanks

thankx sir i'll get the concept

love real whiteboard lessons like yours..... my professors are dull and just run powerpoints during lectures half asleep.

Excellent

Annova test-- when we have one numerical variable and categorical variable where categorical variable has more than two categories T-test-- when we have one categorical variable and one numerical variable where categorical variable has only two categories or one continuous variable one sample praportional test-- when we want to campare values from only one sample(sample is categorical) chi-square test-- when we have two categorical sample correlation test-- when we have two numerical variable

thanks sir u clear my all doubts. plz sir make a video on pearson chi square

THANK u sir Good explanation

Tricks are pretty cooool...

good explanation

Thank you a lot

Great!!!

krish, I have observed that you mentioned to use T - test for two numerical variables and again you mentioned correlation test.

Explanation was very good. I would like to know if my assumptions mentioned below are valid. Hope you acknowledge this. 1. select k best can be applied on both classification and regression problems 2. T-Test can be applied on a categorical feature which has only 2 distinct categories and when sample size is < 30 3. Z-Test is same as T-Test but is applied when sample size > 30 4. ANOVA Test is applied to categorical feature which has more than 2 distinct categories 5. T-test, Z-Test & ANOVA tests are applied only when target has continuous values . I.e, when we are working on regression model 6. Pierson Co-relation Co-eff can be applied only on numerical features. It can be applied between a feature & target and also between features If we find 2 features that are not co-related, we can remove one of them. 7. Co-relation matrix can be applied only on numerical features 8. Chi sqr test can be applied only on categorical features