I have been learning and teaching statistics for years but no one is as good as you. Hats Off, You can't imagine how much it helped me. Thank you from the bottom of my heart. and In India, we celebrate 5th September as Teachers Day. So Happy Teachers Day. GURUJI
thanks for the explanation. I am trying to understand intuitively the idea behind type-1 and type-2 errors. I have a few questions. Type-1 error simple means we have incorrectly rejected the null hypothesis where we were supposed to accept it. well, (1) why we don't just set type-1 error to 0, that means we are even stricter in rejecting null hypothesis. (2) now the definition of type-1 error is confusing as type-1 error means we incorrectly reject the null hypothesis which indicates something wrong action of doing and at the same time type-1 error = alpha which means how strict we are in rejecting the null hypothesis which indicates correct action of doing!? (3) considering type-1 error value as how much strict we are in rejecting the null hypothesis which means alpha is small. In turn this means 1-alpha is larger (the area of accepting the null hypothesis) which indicates we suppose to accept the null hypothesis, not to reject?!
Hello, Justin. You look like an awesome Mediterranean Pirate 🏴☠️🦜💰🚢🔱 here, and I guess Knowledge is the treasure that many have been seeking, and arrived here to find it. Well done! Pieces of eight...
Justin, i have accepted the nul hypothesis that my stats teacher does not influence my stats knowledge and i rejected the nul hypothesis that you don't influence my stats knowledge. Thanks for everything!
Justin! I think this is one of the best ways to intuitively explain the two types of errors. Many thanks for the explanation :) By the way, I feel that the number of views for such an excellent video is low at the time of writing this. I hope more people will benefit from this video. Good luck to you for the journey ahead!
Sir, that was clearest explanation of type 1 and type 2 errors. I've been dealing with statistics for a long time as a test and evaluation professional and your videos will be mandatory viewing for my colleagues who are not as intuitively statistically inclined. Thank you so much. This is an A+ explanation and I hope other people appreciate it as much as I Have an awesome day where everything goes your way.
I am going to watch a lot more of your videos. Thank you so much. I am taking an online statistics course for my Psy D and was struggling with learning with just reading. I needed a visual aide in my learning. This is exactly what I was looking for. Thank you again.
Hi Justin, thanks for the video. I have a question, you might have given an example in one of your videos, but I haven't seen it so maybe you haven't yet. It would be great if you could tell me how to calculate the ideal sample size n when running a hypothesis test and keeping in mind the given value of alpha (i.e: 5%) and beta (i.e: 10%). I'm going a bit crazy with this question. Thanks in advance!
A big dislike to the background music! I couldn't watch the video because of the annoying music. Why do I have to listen to music while learning about statistics? It makes no sense!!!
So just to confirm what you are plotting is the distribution of sample means rather than the population distribution itself? Which is why the curves get more narrow with increased sample size
Sir you're phenomenal. Loved how you used animations to destruct a very technical topic. My college prof woofed this topic within 5 minutes; glad I hoped here... learned a lot. Thank you so much!
Great informative video, thanks! One question - if an OMNIBUS result is significant, leading you to follow up with post-hoc tests, such as a t-Test with some kind of family wise error rate adjustment such as bonferroni, why bother with the OMNIBUS test in the first place? Couldn't you just run the t-Tests and adjusted the p-values for the number of tests (as per bonferroni), making the OMNIBUS redundant. I appreciate for lots of groups this would be time consuming but is that the only benefit of an OMNIBUS approach in that case? I can't see how it offers any extra protection against type 1 error if the end result is to run t-Tests and report corrected p-values anyway? Appreciate any thoughts! Thanks and keep up the great vids.
Can't ask for more, really. In a span of 15 minutes, I got more answers here than reading a bunch of articles and the textbook chapter. Simple as that. Thank you, for real! You made up my day!
Having a distractingly handsome tutor explain attempt to explain Type I & II errrors does not, in any way, make it easier to understand and comprehend!!!
Justin you are damn good in teaching stats. Your videos are high value. I like your clean neat cut videos. How you make these presentations, do let us know about that too, if possible make a video on how to make a quick clean video. And finally all the best and a happy new year to you..
amazing explanation. I already thought it would take me a weeks to understand statistical power, and with these overlaping curves I understood it on the spot!
Great lesson ! I have a question though. Is the power of the test the remaining area under the yellow curve after subtracting both hashed areas (for the Error type I and Error type II) or only the hashed area for Error type II ? It seems logical to me that both hashed areas should be considered. But I am not sure if my interpretation is correct. Thanks in advance, hope an answer on that can come !
@@zedstatistics Yea, that would rock - use the Z logo? Throw in a few Greek Letters so looks like a Frat/sorority ...... could be a hot holiday fashion item. Make it something Prof's give out as extra credit - so Z has to be earned
I have been learning and teaching statistics for years but no one is as good as you. Hats Off, You can't imagine how much it helped me. Thank you from the bottom of my heart. and In India, we celebrate 5th September as Teachers Day. So Happy Teachers Day. GURUJI
In China, we celebrate Teacher's Day every year on Sep. 10. Thank you, Zed!!!!!!
Watched three other videos. This one was the best
Justin, these videos are superbly helpful. Thanks for sharing.
this is my alternate lec for data2002 for sure
thanks for the explanation. I am trying to understand intuitively the idea behind type-1 and type-2 errors. I have a few questions. Type-1 error simple means we have incorrectly rejected the null hypothesis where we were supposed to accept it. well, (1) why we don't just set type-1 error to 0, that means we are even stricter in rejecting null hypothesis. (2) now the definition of type-1 error is confusing as type-1 error means we incorrectly reject the null hypothesis which indicates something wrong action of doing and at the same time type-1 error = alpha which means how strict we are in rejecting the null hypothesis which indicates correct action of doing!? (3) considering type-1 error value as how much strict we are in rejecting the null hypothesis which means alpha is small. In turn this means 1-alpha is larger (the area of accepting the null hypothesis) which indicates we suppose to accept the null hypothesis, not to reject?!
You're too good at explaining this. I came for a refresher and went out understanding even better than I did back when I studied this.
Hello, Justin. You look like an awesome Mediterranean Pirate 🏴☠️🦜💰🚢🔱 here, and I guess Knowledge is the treasure that many have been seeking, and arrived here to find it.
Well done! Pieces of eight...
My favourite review ever.
All this information was scrambled in my head, but you have just completely straightened it out for me! THANK YOU SOOOOO MUCH!
Justin, i have accepted the nul hypothesis that my stats teacher does not influence my stats knowledge and i rejected the nul hypothesis that you don't influence my stats knowledge. Thanks for everything!
I hate to be this person, but you never accept the null hypothesis, only fail to reject it.
@@danielchacreton2401 Guess there is always room to improve you are right haha
Best of luck Justin! Wish you all the success out there
Thank you, Daniel! Your videos are the best on Stats. Premium paid course level of value right here
No problem, Sigmund!
H0: Defendent is NOT guilty. Guilty mean positive and NOT guilty mean negative.
Justin! I think this is one of the best ways to intuitively explain the two types of errors. Many thanks for the explanation :) By the way, I feel that the number of views for such an excellent video is low at the time of writing this. I hope more people will benefit from this video. Good luck to you for the journey ahead!
Please humbly accept my first born son as a token of my gratitude.
Sir, that was clearest explanation of type 1 and type 2 errors. I've been dealing with statistics for a long time as a test and evaluation professional and your videos will be mandatory viewing for my colleagues who are not as intuitively statistically inclined. Thank you so much. This is an A+ explanation and I hope other people appreciate it as much as I Have an awesome day where everything goes your way.
I am going to watch a lot more of your videos. Thank you so much. I am taking an online statistics course for my Psy D and was struggling with learning with just reading. I needed a visual aide in my learning. This is exactly what I was looking for. Thank you again.
Excellent explanation man. Thank you so much
Hey
I just randomly found your Channel AND IT IS AWESOME 😎😎
KEEP MAKING...
This universe needs you
I am eager to watch all your videos
❤️
Your videos are so amazing! When I 'get' something, I'm so happy I could cry! You absolute flippin legend
you're saving my life rn king
Love your emphasis on intuitive learning. Best video I found on understanding the intuition behind hypothesis testing. Keep it up!
Too good man. The whole day I spent in watching your videos and I loved them.
Great content!! Thank you :)
From not understanding anything in my book to somewhat understanding the stuff! thanks for the awesome explanation!
Hi Justin, thanks for the video. I have a question, you might have given an example in one of your videos, but I haven't seen it so maybe you haven't yet.
It would be great if you could tell me how to calculate the ideal sample size n when running a hypothesis test and keeping in mind the given value of alpha (i.e: 5%) and beta (i.e: 10%). I'm going a bit crazy with this question. Thanks in advance!
Exceptionally explanatory and illustrative! Thank you so much for your excellent video!
Besssttttt videoooo.....I just came home frustated from a class and searched up ur video.....u cleared my doubts way better than our proff.....😭
What have we done to deserve two videos from zedstatistics in one week?
A big dislike to the background music! I couldn't watch the video because of the annoying music. Why do I have to listen to music while learning about statistics? It makes no sense!!!
this guy literally save my grades. All of his videos are great at studying the intuition behind inferential statistics
So just to confirm what you are plotting is the distribution of sample means rather than the population distribution itself? Which is why the curves get more narrow with increased sample size
Please create a video on t distribution mean and variance and also F distribution and related to t and chi square🙏🙏🙏🙏🙏🙏. Please hear my prayers!
i love how intuitive and simple math can become with your explanation. thank you so much
Math?
bless you, before i used to memorize and i failed, just watched this three times and it's stuck,
Hey thanks for really nice explanation. May I ask which software you use for presentation.
Justin, I am taking Psychology Statistics and I’m learning this and taking test on it.
So it concludes that one should increase the number of sample tests to make test more effective and thus have confidence in significance level
great explanation, thank you! I'm doing my masters right now and have no experience with stats so your videos are helpful.
I'm in. Thanks for opening the window, clicking the link now.
Excellent video, thank you, Justin.
Excellent content! Thanks a lot for the effort and the smooth way of explaining the concepts!
This was a great way of explaining Type I vs Type II error! Thank you
This is the best explanation on this topic and it really shows how amazing a teacher you are.
I am a stats student but after watching your videos I am now stats curious.
Your channel has best videos on statistics love from india❤️
shouldn't the null hypothesis say: Defendant not guilty?
how much power is desirable in a statistical test?
Please do a video on one-sided sided and two-sided tests
you r the best teacher in this world justin
Why does it use the word power and significance
best explanation I've seen so far!! 👍👍
Thanks alot,
Best explanation ever
Sir you're phenomenal. Loved how you used animations to destruct a very technical topic. My college prof woofed this topic within 5 minutes; glad I hoped here... learned a lot.
Thank you so much!
Sir, please provide all the eight videos on survival analysis. I'm not able to find on your RUclips channel. Please provide. Thank you sir...
Great informative video, thanks! One question - if an OMNIBUS result is significant, leading you to follow up with post-hoc tests, such as a t-Test with some kind of family wise error rate adjustment such as bonferroni, why bother with the OMNIBUS test in the first place? Couldn't you just run the t-Tests and adjusted the p-values for the number of tests (as per bonferroni), making the OMNIBUS redundant. I appreciate for lots of groups this would be time consuming but is that the only benefit of an OMNIBUS approach in that case? I can't see how it offers any extra protection against type 1 error if the end result is to run t-Tests and report corrected p-values anyway? Appreciate any thoughts! Thanks and keep up the great vids.
I LOVE YOU I FINALLY UNDERSTOOD IT YOURE. THE BEST
@Lauraa:
Even though I am a vegetarian who does NOT consume eggs, I would say Justin has made you a nice omelette (metaphorically). Happy Learning
Does smoking cessation improve lung function?
H0: No effect of smoking cessation on lung function
H1: Smoking cessation improves lung infection
thats great content man! very clearing
thank you so much for all the videos, i would appreciate if you provide us with videos on systematic review and meta analysis, STATA, R....
Can't ask for more, really. In a span of 15 minutes, I got more answers here than reading a bunch of articles and the textbook chapter. Simple as that.
Thank you, for real! You made up my day!
Eloquent. You are great, brother..
Having a distractingly handsome tutor explain attempt to explain Type I & II errrors does not, in any way, make it easier to understand and comprehend!!!
Excellent content, th
Thank you! Super helpful!
okay not kidding ive seen my lectures like 5 times and this video explained more than any of those 2 hr lectures TT
You're really helpful!! thank you!
Justin you are damn good in teaching stats. Your videos are high value. I like your clean neat cut videos. How you make these presentations, do let us know about that too, if possible make a video on how to make a quick clean video. And finally all the best and a happy new year to you..
amazing explanation.
I already thought it would take me a weeks to understand statistical power, and with these overlaping curves I understood it on the spot!
thank u very much good work
Can u make a video on Parametric tests
It's all fun with study.. You made it really simple my friend. Loved it.
Hi
If you can please tell me why we check for significance of regression coefficients despite having R square value?
Great lesson ! I have a question though. Is the power of the test the remaining area under the yellow curve after subtracting both hashed areas (for the Error type I and Error type II) or only the hashed area for Error type II ? It seems logical to me that both hashed areas should be considered. But I am not sure if my interpretation is correct. Thanks in advance, hope an answer on that can come !
Thank you for all your efforts.
how easy you explain every complicate subjects.thanks a lot...
excellent explanation.. i wonder how detailed you have studied the subject. love the dedication
The best explanations I have come across - and I have watched many videos! Thanks!
This was explained so well!!
I finally got that! thank you :)
I am screwed in interpreting my data.. u are good.. I am going to see many more videos I guess.. thanks, u explain really well
Oh this is wonderful..thx
You deserve to see the front step of heaven
This the only video I found that explains this concept clearly and correctly!
Thank you so so much for this
What books on Statistics you recommend everyone should check out? Or what books made you so good at stats?
hey justin can you consider making a gamma distribution video? thanks for the vids!
Amazing Explanation!
Stat curious. Love it! 😆
Finally understand power and how it’s related to detecting a difference and sample size
great videos and good luck with the website. I'm sure it'll be awesome.
Exceptional!
Cool stuff and will pass on to students - they need the Zed!
"You need the Zed".: potential t-shirt merch slogan
@@zedstatistics
Yea, that would rock - use the Z logo?
Throw in a few Greek Letters so looks like a Frat/sorority ...... could be a hot holiday fashion item. Make it something Prof's give out as extra credit - so Z has to be earned
Good video! Thank you
Best explanation ever. The null hypothesis that Zeds videos make no difference is disproven - with Power to spare. 😊
One high five for the compliment, another for doing it in statistics :)
Btw, Null Hypothesis can never be disproven!!
Thank you 🙏so much!
This Presentation is really informative.Would you mind drawing a diagram that shows in two-tail tests?
you are the best one explaining this !!!!!! geez
so well said.
Noce EDU
THANKS
This may just help me pass my exam for the 3rd time! Thank you!
I like your videos Justin.