A biiiig shout out from Singapore, Justin! I am a frequent UA-cam user for educational purposes but I was so selfish that I had never left a single comment on the videos I viewed, but I really wanted to give a comment this time and say I sincerely appreciated your videos. It is so important to teach the learners the concepts first, develop their intuition and help them understand the logic behind the concepts. I feel like I have learnt nothing if the teacher says "oh just use these formulas and you don't need to know why they work, but they just work in this and that situation". However, in order to address real-world problems, you need to know what are the tools you have and apply them correctly. As computer science and AI develops, it looks like we can skip many steps to achieve a goal, so understanding a concept thoroughly seems very time and energy consuming and even not as important, but those who hold onto the basics of their areas have the amazing power to create and develop, and they are fast learners and flexible enough to survive in this rapid-changing world. Apologize if my blah-blah sounds very old school or even stupid, but I personally enjoy videos like this and I can't wait to watch more of your videos. Hope you can continue spreading knowledge this way!!
Thanks for this Vicky! What a beaut of a message! I'm adding more and more videos all the time if ya check my website. Messages like this add fuel to the fire :)
I love how you build on the basics! I can’t expect to understand the hard stuff if I don’t establish a foundation first. You explain all of this so well. Deeply grateful!
Wish now and back then when i was in school that you could have explained maths, stats like this! You basically explain the invention and reasoning of the formula. Teaching cant get any better!!!
Definitely, the best explanation for Hypothesis that I have ever seen. Being honest this concept for me is really contradictory, and it has been a little difficult for me to understand. But your videos had helped me a lot. Thanks.
Dear Justin. Very well made videos. I really like your approach. You first give an intuitive understanding and then jump into the Math. I agree with Vicky Lin - conceptual understanding is so very impt before you jump into the tools. Sadly though, neither the students are interested, nor the teachers have the ability to break down esoteric concepts in simple language, like the way you do.
Hi. Thanks much for your videos!!! Could you please explain next quiz: in the first example you've taken two samples each n=20 and came up with sample mean lower then Ho, but presume that first random sample gave us mean that is close to Ho. Does it means that we need to stop further sample taking and decide that Ho is true?
amazing made such a vedio so simple and crystal clear thanks for spending time to make this for everyone.. I loved watching it.. Gratitude for your effort and time
Hi Justin, thank you so much for these videos!! Helps me a lot with studying for the CFA exam. I have a question @ 7:46. You mentioned that when Z is close to 0, the sample is more or less a true representor of the population. I just looked at the Z-table and when Z=0, the confidence level=50%. I don't get that. If Z=0, the sample is close to the population so why would the confidence level be = 50%? Instead, shouldn't it be close to 100% confidence level because you are confident that the sample is almost same as the population so your confidence level would be high? does that make sense?
I have a doubt here. We learnt that we always assume we are wrong (take the opposite of our assumption as the null) and then we try to disprove that statement. Why did we not do that here?
3:13 Why is the question "Is our sample mean far enough away from a 175 cm for us to be able to reject the null hypothesis?" rather than "Is our sample mean close enough to a 175 cm for us to be able to accept the null hypothesis?"... Kind of weird isn't it?
If the null hypothesis can never be PROVED then how come we're saying it's TRUE for the Type 1 Error? Doesn't that TRUE signal a kind of permanency of fact?
"you can never prove"!. That's correct so far as taking a sample is concerned. However, let's assume we lived in a population where every person on the planet would accurately report their height ( and measure babies heights, etc). Boom! the average height could be proved. In other words, there would be no margins of error (assuming we agreed to limit the measurement. e.g., millimetres). Of course, back in the non-hypothetical "real" world, getting everyone to accurately report their height isn't currently practical. FYI, ThX for the very well explained stats :-)
11:11 type 2 error: our mean should be far away from 175 in the rejection region. Then if we do not reject it that would be type 2 error... No? Why did he say otherwise? Someone please guide me
My hypothesis of the day is, how many statistics students actually learn from the verbosity of math/statistics teachers? Im enjoying your videos, but my point is, statistics is just identifying life examples, but who cares? 😂. Only if youre going to work in a lab somewhere, for a company willing to pay for data to support their bottom line, would you need so much digging in. Universities love torturing students. 😄. BTW, your examples are great. Thank you!
A biiiig shout out from Singapore, Justin! I am a frequent UA-cam user for educational purposes but I was so selfish that I had never left a single comment on the videos I viewed, but I really wanted to give a comment this time and say I sincerely appreciated your videos. It is so important to teach the learners the concepts first, develop their intuition and help them understand the logic behind the concepts. I feel like I have learnt nothing if the teacher says "oh just use these formulas and you don't need to know why they work, but they just work in this and that situation". However, in order to address real-world problems, you need to know what are the tools you have and apply them correctly. As computer science and AI develops, it looks like we can skip many steps to achieve a goal, so understanding a concept thoroughly seems very time and energy consuming and even not as important, but those who hold onto the basics of their areas have the amazing power to create and develop, and they are fast learners and flexible enough to survive in this rapid-changing world. Apologize if my blah-blah sounds very old school or even stupid, but I personally enjoy videos like this and I can't wait to watch more of your videos. Hope you can continue spreading knowledge this way!!
Thanks for this Vicky! What a beaut of a message! I'm adding more and more videos all the time if ya check my website. Messages like this add fuel to the fire :)
12 minutes and 41 seconds well spent. Thank you for this excellent video.
this non mathematic video lead me to discover the next videos.
Thats what makes this presentation so enticing
THanks prof you made me enjoy Statistics
I love how you build on the basics! I can’t expect to understand the hard stuff if I don’t establish a foundation first. You explain all of this so well. Deeply grateful!
You're literally saving lives. Thank you.
the production quality of these videos is just immaculate!
what a great teacher , brilliant , very clear pronunciation so that easy to understand each word and the way you explained is marvelous
I referred your videos to my friends. You explain amazing.
Thank you for providing such a clear explanation and introduction to Hypothesis testing.
Wish now and back then when i was in school that you could have explained maths, stats like this! You basically explain the invention and reasoning of the formula. Teaching cant get any better!!!
Well explained and easy to comprehend. Thank You!
Nobody could have explained it more easily than this. Thank you so much💯
Definitely, the best explanation for Hypothesis that I have ever seen. Being honest this concept for me is really contradictory, and it has been a little difficult for me to understand. But your videos had helped me a lot. Thanks.
Thank you so much for explaining the concept so well!
You are sooo good.. wish I had some one who taught stats like You do.. brilliant work man.. sooo good..! Thanks for all the efforts of making video
Great video. Please make more of such videos which are easy to understand
Just a small thanx for making it easier to digest!
Great Video! Can't wait to get an A on my upcoming test!
Awesome guide for begineers... Loved other playlist too.
Dear Justin. Very well made videos. I really like your approach. You first give an intuitive understanding and then jump into the Math. I agree with Vicky Lin - conceptual understanding is so very impt before you jump into the tools. Sadly though, neither the students are interested, nor the teachers have the ability to break down esoteric concepts in simple language, like the way you do.
Hi. Thanks much for your videos!!! Could you please explain next quiz: in the first example you've taken two samples each n=20 and came up with sample mean lower then Ho, but presume that first random sample gave us mean that is close to Ho. Does it means that we need to stop further sample taking and decide that Ho is true?
Thank you for making life easier.
amazing made such a vedio so simple and crystal clear thanks for spending time to make this for everyone.. I loved watching it.. Gratitude for your effort and time
Finally God has descended for solving problems of us students ❤
Thank you very much. This is very helpful.
Good breakdown
thank you sir for this explanation.
Hi Justin, thank you so much for these videos!! Helps me a lot with studying for the CFA exam.
I have a question @ 7:46. You mentioned that when Z is close to 0, the sample is more or less a true representor of the population. I just looked at the Z-table and when Z=0, the confidence level=50%. I don't get that. If Z=0, the sample is close to the population so why would the confidence level be = 50%? Instead, shouldn't it be close to 100% confidence level because you are confident that the sample is almost same as the population so your confidence level would be high?
does that make sense?
this is amazing. thanks a lot!
I have a doubt here. We learnt that we always assume we are wrong (take the opposite of our assumption as the null) and then we try to disprove that statement. Why did we not do that here?
3:13 Why is the question "Is our sample mean far enough away from a 175 cm for us to be able to reject the null hypothesis?" rather than "Is our sample mean close enough to a 175 cm for us to be able to accept the null hypothesis?"... Kind of weird isn't it?
would you mind sharing what program you use to present your information? has a great mind map feel with slide show vibes
If the null hypothesis can never be PROVED then how come we're saying it's TRUE for the Type 1 Error? Doesn't that TRUE signal a kind of permanency of fact?
"you can never prove"!. That's correct so far as taking a sample is concerned. However, let's assume we lived in a population where every person on the planet would accurately report their height ( and measure babies heights, etc). Boom! the average height could be proved. In other words, there would be no margins of error (assuming we agreed to limit the measurement. e.g., millimetres).
Of course, back in the non-hypothetical "real" world, getting everyone to accurately report their height isn't currently practical.
FYI, ThX for the very well explained stats :-)
Why don't we take into account the value of N (the size of the population) when calculating z ?
I love these videos!
Loved this..
Dear Prof. Please explain why do we use probability in inferential statistics and hypothesis testing and why always use Normal Distribution
11:11 type 2 error: our mean should be far away from 175 in the rejection region. Then if we do not reject it that would be type 2 error... No?
Why did he say otherwise? Someone please guide me
Amazing 🎉
Wonderful, thank you
nice video sir,
only one doubt how and who decides the critical value
You choose it by choosing alpha 10:25
Great video thanks
Excellent ❤️
awsm video
Really Good
Thank you.
My hypothesis of the day is, how many statistics students actually learn from the verbosity of math/statistics teachers? Im enjoying your videos, but my point is, statistics is just identifying life examples, but who cares? 😂. Only if youre going to work in a lab somewhere, for a company willing to pay for data to support their bottom line, would you need so much digging in. Universities love torturing students. 😄. BTW, your examples are great. Thank you!
Thanks!
thank you
Thanks
awsome
you the fucken best.
Tx sir
Thank u
legend
Wow!
Excellent 🤩
Thanks