All the generous people who post educational videos on UA-cam should get the Nobel Prize. The video that is posted in 2012 is still so fresh in 2019 and is going to help may students and researchers. Thank you.
Yesterday I wrote an Excel VBA program to parse my last 200 online backgammon matches, consisting of more than 17,000 dice rolls. I wanted to prove the dice rolls generated were not biased. I plan on making the program available to others who play online, so they can analyze their own matches. I've been spending the better part of the day learning about what a chi test is, and how it works, via all of the many website articles and videos available. In my opinion, this video is the best I've looked at so far. (And I've looked at a lot.) THANKS!
The PROBABILITY of me passing my stats class was before watching your videos was slim to none. I was near my BREAKDOWN POINT. You sir, are not your AVEAGE Joe, I MEAN, the biggest COMPLEMENT I can give you is that my CONFIDENCE LEVEL has been TRANSFORMATIONAL and POSITIVE. Thank you very MUch!
Thank you so much. I found your video because my online course instructor has done nothing to teach the materials he covers in his course. You deserve the money for this more than the university I'm attending.
Oh thank you! I appreciate your comment. But it is YOU that are awesome. :) Making the effort and taking the time to come on here and learn and grow is what is AWESOME. I have two videos on One-way ANOVA in Playlist 13. If you go to my channel page and click on "Playlists" you should see it there. I have a two-part video on Two-Way "Block" ANOVA under development and hope to have at least Part 1 uploaded this weekend. I've just been crazy busy. All the very best Sini! - B
Such a bummer I just now discovered your channel a week before my stats final! This is by far the clearest and most concise explanation of the Chi-Square test I have found. Thank you!
I'm always excited when I realize you have video for one of the topics that I am going over in class. As soon as I saw this one, I thought, "thank, God! I'm going to understand this part." LOL... Thank you for your help.
I like the words of encouragement you give in the beginning of each video, I needed the belief in me. By the way, I got my first "A" on stat quiz and your videos were a large part of that, Thank you Brandon.
I wanted to say thank you for making these great videos I am about to start a Psychology degree and I was highly intimidated by the statistical side of things but your way of explaining things has helped so much THANK YOU
Absolutely great video.... I needed to apply this test, remembered it from school (ha,ha.....over 40 years ago) and you totally brought the basics back, and I'm confident I can apply the ideas to my particular needs! One pet peeve: (remember I'm an old guy, and in addition have taught physics (for example) part time for years in addition to my day job: I wouldn't call the correct pronunciation of a word like 'CHI' 'first and foremost.' If someone calls it chy squared test or shy squared test, the chances of a misunderstanding are 0.01% (we could run a chi squared test if we did a real world study!). The issue is that chi is misspelled in English and should be "ky." I have a Slavic name and have learned to accept that people pronounce it phonetically as it is easy to know what they mean. Anyway...just a pet peeve. Super great tutorial, and I''ll be sure to look at more your tutorials....wish I had you for a teacher way back then (but you would have been a bit young, I think! .
can u plz do a video on Paramteric Vs Non parametric test. Also , on non-parametric test like Mc Nemar, Fisher Exact , Blandman Goldman analysis, Cronback alpha etc. The SPSS software has chart which gives the listing of the different groups. Also, please add biostatistical concepts to it like odds ratio etc. Also, last but not the least an alogorithm how to apply which test when we have certain situtaion and data.
Hi Brandon!! very helpful video thanks !! But wanted to point this out that we never 'accept' a hypothesis. We only 'reject' or 'do not reject' a hypothesis.
ATTENTION EVERYONE There is a mistake in the video. 33:24 The test is just saying that "we can't be 99% confident that the die is loaded". It doesn't say "we can be 99% confident that the die is fair". This wouldn't make any sense, since we already are 95% confident that the dice IS loaded from the previous test.
Hi Brandon, thanks for these videos, they are really good. I am not sure that you are still online (alive) because I could not see you to answer recent questions but hope all is good with you. I do not understand one thing (at least) about your explanation regarding the chi-square: You said in one video that chi-square =(n-1)s2[meaning:squared]/sigma2[meaning:squared] and then you used this formula: chi-square=sum[(O-E)2[meaning:squared]/E]. (Sorry for the typos.) So I have no idea where these formulas came from and how they can be equal? If you are out there, could you please give me an explanation? Thanks.
Big thanks from South Korea Brandon, it really helped me understanding general understanding of Chi-square test! But just a quick thing that I don't really understand, you mentioned that raising the strictness(p-value), we all of sudden 99% confident that it is fair die. So we don't reject null hypothesis. I get it theoretically because Chi-square critical value of 99% confidence level, it is 15.09 so our x^2 isn't above the Chi-square critical value. But if you think more rationally...Actual data hasn't changed but how could you 95% sure that it was loaded die but now 99% confident that it is a fair die???? how was this possible? Just doesn't make sense rationally.
@Brandon , At 34:14 , The slide says "We must accept Ho" but isn't it "We fail to reject Ho" instead ? Is there any specific reason why you mentioned to accept it ?
A diagram would REALLY help the point being made at the end of the video when the selected confidence level is arbitrarily changed for the same observed data to reverse the decision about the hypothesis.
I've been watching all the playlists and they are great. This video has the first statement that I disagree with. When you use p = 0.01 you say that you are 99% confident that the die is fair. This seems wrong to me, because when you increase the nonrejection region, you should only say that you increase confidence if the chi² value is outside the nonrejection region. If that were not the case, we could call any die fair by using an arbitrarily high nonrejection region that includes higher values of chi² and say that we are 99.999999% sure that the die is fair. Does that make sense? Thank you very much for the videos.
I think you are correct. The test is just saying that "we can't be 99% confident that the die is loaded". It doesn't say "we can be 99% confident that the die is fair". This wouldn't make any sense, since we already are 95% confident that the dice IS loaded from the previous test.
Hi Brandon.. Thank you so much..your videos are really really helpful in understanding the basics....not sure if you have your videos on Factor Analysis including Exploratory, Principal Component analysis...If yes..Pls let me know...if not can you pls explain these topics even...? Thanks
Very good presentation! Helped a lot to see examples visually and you explained very well. Was also looking for logistic regressions, multiple regressions, factor analysis, MANOVA, DFAs in your playlists. Hope to see your new videos soon!!
Hi. Thank you for your videos. They are great. I have a question. I can see where we can get an expected value for the dice since they are have only six sides, but how could we get an expected value for your first example with freshman, sophomores, juniors, etc. Where would we get an expected value there? Maybe an expected value would be just the average over time? Thanks
you're final conclusion is incorrect should be "At the 99% confidence level we are not sure if the die is loaded". The die having a 99% chance of being unloaded makes no sense when previously it was 95% chance of loaded. We can only reject the Null, not accept the alternate.
Hi Brandon ....your videos are excellent it really helps for freshers...can you guide which videos to refer for "tests of goodness to fit and independence"
Hey Brandon, I loved this explanation, but have a quick question. I understand the math and the mathematical explanation as for what happens when you change the P value and how it impact the end result. But logically - I'm struggling. You referred to P value as the level of confidence. So when changing the P value from 95% to 99% it's as if I'm looking to find a much more confident answer - or strict as you said. My struggle is that it seems as if I'm 95% confident that I have the loaded die, and 99% confident that I have the fair one. Although you attempted to explain this in the video - I still don't feel that I understand the logical explanation.
It's best to 'reject' or 'fail to reject' the null hypothesis, rather than 'accept' the alternative hypothesis. In the first instance, you might say there is enough evidence at the 95% confidence level to reject the null hypothesis. There is not enough evidence to reject the null hypothesis at the 99% level. In other words, if I only need to be 95% confident, I'll reject the assumption that my die is fair. But if I need to be 99% confident about it (maybe I'm about to accuse a good friend of cheating), I won't throw out the null hypothesis. In the second instance, the stakes are too high and there's more than 1% chance that I could be wrong.
A very helpful video, thanks a lot! I am new to statistics and I have decided to use chi-square in my research to determine statistical significance. I chose the threshold of .05, meaning that p below .05 indicates significance. But I don't get one thing. For example, I have data which claims that people in the south pronounce potato in one way and again a certain number of people pronounce the other way. Similarly, a certain number of participants in the north pronounce potato in one way while a certain number pronounce the same word the other way. Si i can use chi to see if there is a significant difference in pronunciation between the two groups of northern speakers. Also, the same can be done with southern speakers. I can also combine the northern and southern speakers to see if there is a statistical significance in pronunciation in general. But what am I left with if i calculate the chi-square for the whole of the chart (two groups of northern and two groups of southern speakers, which constitute a chart of two rows and two columns )? I know this is a long comment but maybe someone can help me out and tell me if I am heading in the right direction.
Hi Brandon, I love your explanations and examples. One thing I don't understand is whether failing to reject and accepting the NULL hypothesis are same. As per my understanding, We can only reach a conclusion and accept the alternate hypothesis if we reject the NULL hypothesis. If we fail to reject the NULL hypothesis then we cannot conclude anything. But in your video you have mentioned, since we fail to reject the NULL hypothesis at significance level of 1%, we must accept NULL Hypothesis and conclude that the die is fair.
You're right: failing to reject is not the same as accepting. If a court fails to show that someone is guilty, this does not necessarily mean the accused is innocent. It only means we do not have enough evidence to conclude that the person is guilty.
I took 3 stats classes in my undergrad and grad work knowing that I would never need to "Do" stats, but only understand the very basics later in my career. Huh! 30+ years later I have to do stats. Thank you for your videos. I have gone through Chi Squared video #1 twice. Now I am working on Video 2. If I have a basic question, can I include it here? For example, when I increase my Confidence from 95% to 99.9% shouldn't my P value go up? I used Excel formula =chisq.inv(0.05,3) with result 0.710723. But =chisq.inv(.001,3) resulted in 0.090804
Do you have Mann u Whitney / Wilcoxon signed rank test/ or Kruskal Wallis test you have basically taught me my whole stats class because you are an awesome teacher but I can't find these nonparametric tests or anything comparing parametric to non parametric
Hi Sir, you are awesome! I was looking for your lecture about Anova, regression and all could not find them. Please upload them if you have them. You expalin very nicely! thanks
Is this the same as chi square goodness of fit test? (also psa thank you soo much for making these videos it is helping me tremendously in clearing concepts)
So can I conduct the chi-square test to determine if any significant associations exist between responses in different categories? If so do you have a video demonstrating this procedure.
Hi, Brandon, it's an amazing tutorial, but I'm a little confused that in previous video, you are talking about alpha in the Excel function, but in the video, you are talking about p-value, is p-value possibility related to statistic? if I understand it correctly p-value shouldn't be fixed
Hej! A lot of thanks for these videos. I just noticed that the text in the slide in minute 19:00 says: "I need you to be 95% in your conclusion" 95% what? Maybe that can be improved. Thanks again for your wonderful work!!
HI Brandon, im quite confused now. I was taught that the p-value is the probability of obtaining the observed statistic, or one more extreme, in favor of the alternative hypothesis. The "alpha" was the threshold at which you would either accept or reject the null. In this you made the p-value, or threshold (confused what is true now), more strict and yet now you accept the null?? How could that be if it already failed the test when it was less strict at 0.05%?
I think the conclusion in the example with a p-value of 0.01 is incorrect. I don't think we can say we accept the null hypothesis with 99% confidence. I think the correct interpretation is that we are unable to reject the null hypothesis but that doesn't mean it is true, and it certainly doesn't mean we can be 99% confident it is true. How can the same data set give us a 95% chance that H1 is true, and a 99% chance that H0 is true? Or have I missed something fundamental here? Please let me know if I am!!
I was looking to see if someone had made this comment because I thought the same thing. The videos are still great, of course, but I'm in agreement with you that this part is not correct and your phrasing in your interpretation is correct.
I came to the same conclusion as you. It is incorrect to claim that "We are 99% confident that you have the fair die". What you can claim is that "I can't be 99% confident that the die is not fair, but I can be 95% confident that it is not fair". By trying to increase confidence level from 95 to 99%, you need a stricter criteria, which means the result has to be more outrageous to allow such confidence increase.
I agree with you. Fail tot reject the null hypothesis at the 99 percent confidence level is how I would phrase the conclusion. I guess you van never accept H0...
Brandon I am a medical resident doing research now, what do I need to know as far as statics, is there a link for basics tests.. things.. excel sheet use.. I can't recall the basics from ed school. is there a simple book u'd recommend? thanks boss
Awesome video. I have one question though shouldn't ANOVA be ideal in the case of the first example because one variable is continuous and other variable is categorical? Is there a reason this example was explaining using chi squared test and not ANOVA?
This is excellent, but I have a problem with "I am 95% sure this die is loaded" and "I am 99% sure this die is fair." I am sure you are aware of the problem. I wonder if there is a different way to say the second conclusion. I took college Stat 50 years ago and this is a REALLY good series.
The statament in the video is wrong. The hypotheses are incorrectly enunciated, which leads to a wrong statement even though the math is right. The correct enunciations are: 1) "we can affirm the dice is not loaded". 2) The other must be the complement which is "We can not affirm that the dice is not loaded". So the right statement are: We're are 95% confident that the dice is not lodaded We're not 99% confident that the dice is not lodaded
Thank you very much for your video lecture. It is awesome and very useful. It would be even better if I can download your powerpoint/pdf. XDD I really appreciate your fantastic work! Thank you!
I'm a bit confused about one part: In the end, can we really say that we are 99% sure that we have the fair die? Because I thought that the hypothesis test is only about rejecting or not rejecting the null; that if we can't reject the null then all we can really say is that we can't reject it (based on the sample and p-value). There can still be quite a small probability that the observed variance occurred by chance. It's just that that probability isn't small enough for us to base rejecting the null hypothesis on. Basically, the difference between type I and type II errors, as I understand them. Please correct me if I'm wrong :)
Hello! You are correct. I think I addresses this in other comments. I was speaking colloquially there, easy to slip into when making videos on the fly. Unless we control type II error, we either reject or fail to reject the null.
According to the CHI Square formula, the denominator was supposed to be the Hypothesized variance right? and here it seems like the expected value is taken as the denominator. Will somebody clarify this?
All the generous people who post educational videos on UA-cam should get the Nobel Prize. The video that is posted in 2012 is still so fresh in 2019 and is going to help may students and researchers. Thank you.
Next year: 10th anniversary
true..it makes everything so freshly available to save my freakin time and resources..go to school for some professors who dont kno how to teach
in 2023 too ❤
watching it in 2024
I've read and re-read the chapter in my text book on the Chi Square Test three times before I decided to UA-cam it. You Sir, are my Stats hero.
You are my hero. You are god of Statistics! New School of teaching. University should take notice of your way of teaching. 7 stars!
Yesterday I wrote an Excel VBA program to parse my last 200 online backgammon matches, consisting of more than 17,000 dice rolls. I wanted to prove the dice rolls generated were not biased. I plan on making the program available to others who play online, so they can analyze their own matches.
I've been spending the better part of the day learning about what a chi test is, and how it works, via all of the many website articles and videos available.
In my opinion, this video is the best I've looked at so far. (And I've looked at a lot.)
THANKS!
The PROBABILITY of me passing my stats class was before watching your videos was slim to none. I was near my BREAKDOWN POINT.
You sir, are not your AVEAGE Joe, I MEAN, the biggest COMPLEMENT I can give you is that my CONFIDENCE LEVEL has been TRANSFORMATIONAL and POSITIVE. Thank you very MUch!
Thank you so much. I found your video because my online course instructor has done nothing to teach the materials he covers in his course. You deserve the money for this more than the university I'm attending.
Oh thank you! I appreciate your comment. But it is YOU that are awesome. :) Making the effort and taking the time to come on here and learn and grow is what is AWESOME. I have two videos on One-way ANOVA in Playlist 13. If you go to my channel page and click on "Playlists" you should see it there. I have a two-part video on Two-Way "Block" ANOVA under development and hope to have at least Part 1 uploaded this weekend. I've just been crazy busy. All the very best Sini! - B
That is so encouraging!
"I'm not going to explain it EITHER" lol. PS. LOVE your videos. They are a godsend plus you have a lovely way about you.
Thank you man, at last I found someone explaining it in a very comprehensive way.
Such a bummer I just now discovered your channel a week before my stats final! This is by far the clearest and most concise explanation of the Chi-Square test I have found. Thank you!
One of the best and most helpful explanations I've found for this topic on the internet!
I'm always excited when I realize you have video for one of the topics that I am going over in class. As soon as I saw this one, I thought, "thank, God! I'm going to understand this part." LOL... Thank you for your help.
Hahaha same happens with me
My new best friend, graphs. Thank you so much for this video!
Thank you so much Mr. Foltz. I am taking a Research course at Embry-Riddle Aeronautical University, and your lessons have been very helpful
I appreciate your simplicity of the chi-square explanation.
I like the words of encouragement you give in the beginning of each video, I needed the belief in me. By the way, I got my first "A" on stat quiz and your videos were a large part of that, Thank you Brandon.
You are genuis...You are blessed with a natural way of explaining complex things in the most simplified way!!!! Great going :-)
Thank you for all of your videos! I seriously couldn't have made it through this statistics course without you!
thanks for an educative lecture
Your explanations are very clear. I really appreciate your quality explanations
Mannnnnnn... I just want to say THANK YOU! Because of you I'm getting through my statistics class!
BIG
You really know how students' brain function.. Thank you for this tutorial.. God bless..
This is by far the best video on this topic
You are getting me through my stats class! THANK YOU!!!
That was excellent. You are one of the best professors in the world. Thank you.
This is the best video for Chi-square I've ever seen! Thanks, Brandon.
U sir make it so simple to understand. Respect.
I wanted to say thank you for making these great videos I am about to start a Psychology degree and I was highly intimidated by the statistical side of things but your way of explaining things has helped so much THANK YOU
That was a great video. I like the audio quality and style of teaching. Thank you,
Aspiring Data Analyst
You are very welcome Nikhil! I am glad you found it helpful. Keep learning! Best, B.
Fantastic video. Truly for beginners. Great section at the end on what p-value actually means.
Absolutely great video.... I needed to apply this test, remembered it from school (ha,ha.....over 40 years ago) and you totally brought the basics back, and I'm confident I can apply the ideas to my particular needs! One pet peeve: (remember I'm an old guy, and in addition have taught physics (for example) part time for years in addition to my day job: I wouldn't call the correct pronunciation of a word like 'CHI' 'first and foremost.' If someone calls it chy squared test or shy squared test, the chances of a misunderstanding are 0.01% (we could run a chi squared test if we did a real world study!). The issue is that chi is misspelled in English and should be "ky." I have a Slavic name and have learned to accept that people pronounce it phonetically as it is easy to know what they mean. Anyway...just a pet peeve. Super great tutorial, and I''ll be sure to look at more your tutorials....wish I had you for a teacher way back then (but you would have been a bit young, I think!
.
Ok, tomorrow I expect to get a good grade on my exam after watching this video!Thanks a lot Brandon!
totally agree with review below! Excellent explanation! Thank you!
Hi Brandon,
I rarely make comment, I just like and dislike videos but you've done an excellent job! Keep up the good work!
GREAT TEACHER Dr BRANDON FOLTZ
hi brandon, just want to say that was genius! none of my class explains that ~ it was really helpful
Hi Brandon, your videos on stats are truly amazing. Do you have videos on Data Analysis?
+Aditya chandra What about Data Analysis are you looking for?
+Brandon Foltz Decision tree, cluster analysis, Naive Bayes. Other aspects required for data analysis..
+Brandon Foltz Do you have videos on Multiple hypothesis testing ?
can u plz do a video on Paramteric Vs Non parametric test. Also , on non-parametric test like Mc Nemar, Fisher Exact , Blandman Goldman analysis, Cronback alpha etc. The SPSS software has chart which gives the listing of the different groups. Also, please add biostatistical concepts to it like odds ratio etc. Also, last but not the least an alogorithm how to apply which test when we have certain situtaion and data.
Thank a lot, Brandon, now I could analyze complex problems.
Hi Brandon!! very helpful video thanks !!
But wanted to point this out that we never 'accept' a hypothesis. We only 'reject' or 'do not reject' a hypothesis.
ATTENTION EVERYONE There is a mistake in the video. 33:24
The test is just saying that "we can't be 99% confident that the die is loaded".
It doesn't say "we can be 99% confident that the die is fair". This wouldn't make any sense, since we already are 95% confident that the dice IS loaded from the previous test.
You are mostly correct. Never accept the null hypothesis. Only fail to reject.
I appreciate the detail. You're a good teacher!
Easy to understand, you make complicated things became easier
this is my new favorite stats channel. i wish you did linear algebra too. and maybe also some programming tutorials like python.
Hi Brandon, thanks for these videos, they are really good. I am not sure that you are still online (alive) because I could not see you to answer recent questions but hope all is good with you. I do not understand one thing (at least) about your explanation regarding the chi-square:
You said in one video that chi-square =(n-1)s2[meaning:squared]/sigma2[meaning:squared] and then you used this formula: chi-square=sum[(O-E)2[meaning:squared]/E]. (Sorry for the typos.) So I have no idea where these formulas came from and how they can be equal? If you are out there, could you please give me an explanation? Thanks.
Big thanks from South Korea Brandon, it really helped me understanding general understanding of Chi-square test! But just a quick thing that I don't really understand, you mentioned that raising the strictness(p-value), we all of sudden 99% confident that it is fair die. So we don't reject null hypothesis. I get it theoretically because Chi-square critical value of 99% confidence level, it is 15.09 so our x^2 isn't above the Chi-square critical value. But if you think more rationally...Actual data hasn't changed but how could you 95% sure that it was loaded die but now 99% confident that it is a fair die???? how was this possible? Just doesn't make sense rationally.
congrats on 200k subs you deserve it homie
you did a great job of explaining this and it really helped me
@Brandon , At 34:14 , The slide says "We must accept Ho" but isn't it "We fail to reject Ho" instead ? Is there any specific reason why you mentioned to accept it ?
It was just a slip in the moment while recording sorry! Unless Type II error is controlled, it's Fail to Reject.
Just an excellent explanation I could ever get .. Thanks a ton
You are such a great teacher
A diagram would REALLY help the point being made at the end of the video when the selected confidence level is arbitrarily changed for the same observed data to reverse the decision about the hypothesis.
I've been watching all the playlists and they are great. This video has the first statement that I disagree with. When you use p = 0.01 you say that you are 99% confident that the die is fair. This seems wrong to me, because when you increase the nonrejection region, you should only say that you increase confidence if the chi² value is outside the nonrejection region. If that were not the case, we could call any die fair by using an arbitrarily high nonrejection region that includes higher values of chi² and say that we are 99.999999% sure that the die is fair. Does that make sense?
Thank you very much for the videos.
I think you are correct. The test is just saying that "we can't be 99% confident that the die is loaded".
It doesn't say "we can be 99% confident that the die is fair". This wouldn't make any sense, since we already are 95% confident that the dice IS loaded from the previous test.
Yeah, I'm confused as well. with the same dice result, we say it is NOT fair with 95% confidence yet we say it is fair with 99% confidence?
Hi Brandon.. Thank you so much..your videos are really really helpful in understanding the basics....not sure if you have your videos on Factor Analysis including Exploratory, Principal Component analysis...If yes..Pls let me know...if not can you pls explain these topics even...?
Thanks
Very good presentation! Helped a lot to see examples visually and you explained very well. Was also looking for logistic regressions, multiple regressions, factor analysis, MANOVA, DFAs in your playlists. Hope to see your new videos soon!!
Thank you. Just what I needed!!
Hi. Thank you for your videos. They are great. I have a question. I can see where we can get an expected value for the dice since they are have only six sides, but how could we get an expected value for your first example with freshman, sophomores, juniors, etc. Where would we get an expected value there? Maybe an expected value would be just the average over time? Thanks
Hi Brandon. Your videos are wonderful and very helpful. Do you have any videos on Wilcoxon tests?
Thank you so much!! I might actually pass this class.
Great videos on this channel!!
One question to this one: Did you actually "change the p-value"? Or did you not rather change the alpha-level?
I like his video a lot. I think once the data is set, p-value is determined, we change alpha-level to decide to reject Ho or not.
you're final conclusion is incorrect should be "At the 99% confidence level we are not sure if the die is loaded". The die having a 99% chance of being unloaded makes no sense when previously it was 95% chance of loaded. We can only reject the Null, not accept the alternate.
Great Videos by the way. Really helps make sense of hypothesis testing. Thanks.
Adam Mills yeah, i caught that too. Could we say that we are 95% plus confident that the die IS loaded, but less than 99% sure.
Awesome Tutorial.Is it possible to get the notes on all these topics? I mean the PPTs that you use during the tutorial?
Hi Brandon ....your videos are excellent it really helps for freshers...can you guide which videos to refer for "tests of goodness to fit and independence"
Hey Brandon,
I loved this explanation, but have a quick question. I understand the math and the mathematical explanation as for what happens when you change the P value and how it impact the end result. But logically - I'm struggling. You referred to P value as the level of confidence. So when changing the P value from 95% to 99% it's as if I'm looking to find a much more confident answer - or strict as you said. My struggle is that it seems as if I'm 95% confident that I have the loaded die, and 99% confident that I have the fair one. Although you attempted to explain this in the video - I still don't feel that I understand the logical explanation.
It's best to 'reject' or 'fail to reject' the null hypothesis, rather than 'accept' the alternative hypothesis. In the first instance, you might say there is enough evidence at the 95% confidence level to reject the null hypothesis. There is not enough evidence to reject the null hypothesis at the 99% level. In other words, if I only need to be 95% confident, I'll reject the assumption that my die is fair. But if I need to be 99% confident about it (maybe I'm about to accuse a good friend of cheating), I won't throw out the null hypothesis. In the second instance, the stakes are too high and there's more than 1% chance that I could be wrong.
Thanks for the explanation. I had the same doubt as itai, you cleared it :-)
A very helpful video, thanks a lot! I am new to statistics and I have decided to use chi-square in my research to determine statistical significance. I chose the threshold of .05, meaning that p below .05 indicates significance. But I don't get one thing. For example, I have data which claims that people in the south pronounce potato in one way and again a certain number of people pronounce the other way. Similarly, a certain number of participants in the north pronounce potato in one way while a certain number pronounce the same word the other way. Si i can use chi to see if there is a significant difference in pronunciation between the two groups of northern speakers. Also, the same can be done with southern speakers. I can also combine the northern and southern speakers to see if there is a statistical significance in pronunciation in general. But what am I left with if i calculate the chi-square for the whole of the chart (two groups of northern and two groups of southern speakers, which constitute a chart of two rows and two columns )? I know this is a long comment but maybe someone can help me out and tell me if I am heading in the right direction.
Hi Brandon, I love your explanations and examples. One thing I don't understand is whether failing to reject and accepting the NULL hypothesis are same. As per my understanding, We can only reach a conclusion and accept the alternate hypothesis if we reject the NULL hypothesis. If we fail to reject the NULL hypothesis then we cannot conclude anything. But in your video you have mentioned, since we fail to reject the NULL hypothesis at significance level of 1%, we must accept NULL Hypothesis and conclude that the die is fair.
hi Divya, have you found an answer, this is confusing, i dont understand why accepted H0
You're right: failing to reject is not the same as accepting. If a court fails to show that someone is guilty, this does not necessarily mean the accused is innocent. It only means we do not have enough evidence to conclude that the person is guilty.
I like this tutorial, is very comprehensive
Does Playlist 12 on Chi -square have more videos? if so how many? could you please help with the links?
Great video, great presentation -thanks!
I am just loving the videos
I took 3 stats classes in my undergrad and grad work knowing that I would never need to "Do" stats, but only understand the very basics later in my career. Huh! 30+ years later I have to do stats. Thank you for your videos. I have gone through Chi Squared video #1 twice. Now I am working on Video 2. If I have a basic question, can I include it here? For example, when I increase my Confidence from 95% to 99.9% shouldn't my P value go up? I used Excel formula =chisq.inv(0.05,3) with result 0.710723. But =chisq.inv(.001,3) resulted in 0.090804
Do you have Mann u Whitney / Wilcoxon signed rank test/ or Kruskal Wallis test you have basically taught me my whole stats class because you are an awesome teacher but I can't find these nonparametric tests or anything comparing parametric to non parametric
Hi Sir, you are awesome! I was looking for your lecture about Anova, regression and all could not find them. Please upload them if you have them. You expalin very nicely! thanks
Is this the same as chi square goodness of fit test? (also psa thank you soo much for making these videos it is helping me tremendously in clearing concepts)
So can I conduct the chi-square test to determine if any significant associations exist between responses in different categories? If so do you have a video demonstrating this procedure.
Hi, Brandon, it's an amazing tutorial, but I'm a little confused that in previous video, you are talking about alpha in the Excel function, but in the video, you are talking about p-value, is p-value possibility related to statistic? if I understand it correctly p-value shouldn't be fixed
awesome lecture keep posting brandon
Makes so much more sense! Thank you.
Hej! A lot of thanks for these videos. I just noticed that the text in the slide in minute 19:00 says: "I need you to be 95% in your conclusion" 95% what? Maybe that can be improved. Thanks again for your wonderful work!!
A very helpful vid, thank you!
HI Brandon, im quite confused now. I was taught that the p-value is the probability of obtaining the observed statistic, or one more extreme, in favor of the alternative hypothesis. The "alpha" was the threshold at which you would either accept or reject the null. In this you made the p-value, or threshold (confused what is true now), more strict and yet now you accept the null?? How could that be if it already failed the test when it was less strict at 0.05%?
Thank you so much. Your videos are the best.
I think the conclusion in the example with a p-value of 0.01 is incorrect. I don't think we can say we accept the null hypothesis with 99% confidence. I think the correct interpretation is that we are unable to reject the null hypothesis but that doesn't mean it is true, and it certainly doesn't mean we can be 99% confident it is true. How can the same data set give us a 95% chance that H1 is true, and a 99% chance that H0 is true? Or have I missed something fundamental here? Please let me know if I am!!
I was looking to see if someone had made this comment because I thought the same thing. The videos are still great, of course, but I'm in agreement with you that this part is not correct and your phrasing in your interpretation is correct.
I came to the same conclusion as you. It is incorrect to claim that "We are 99% confident that you have the fair die". What you can claim is that "I can't be 99% confident that the die is not fair, but I can be 95% confident that it is not fair". By trying to increase confidence level from 95 to 99%, you need a stricter criteria, which means the result has to be more outrageous to allow such confidence increase.
Hi Brandon
In the case where in 95% we reject and in 99% we accept, what would we the "right" answer in real life?
This was perfect for what I needed. Thank you!!!!
Great. Please what's the link for the next video?
Thanks a lot! Very easy to understand!
Hi Brandon, your videos are amazing..is it possible for you to upload videos on calculus, trigonometry?
Can you accept the null hypothesis? It should ideally be fail to reject at 99% and reject the null at 95% right?
I agree with you. Fail tot reject the null hypothesis at the 99 percent confidence level is how I would phrase the conclusion. I guess you van never accept H0...
Brandon I am a medical resident doing research now, what do I need to know as far as statics, is there a link for basics tests.. things.. excel sheet use.. I can't recall the basics from ed school. is there a simple book u'd recommend? thanks boss
Great series !!!
I found sample Proportion and Chi-square test are both for Categorical variables,but
when do we use them ?
So helpful! Thank you!
Awesome video. I have one question though shouldn't ANOVA be ideal in the case of the first example because one variable is continuous and other variable is categorical? Is there a reason this example was explaining using chi squared test and not ANOVA?
This is excellent, but I have a problem with "I am 95% sure this die is loaded" and "I am 99% sure this die is fair." I am sure you are aware of the problem. I wonder if there is a different way to say the second conclusion.
I took college Stat 50 years ago and this is a REALLY good series.
The statament in the video is wrong. The hypotheses are incorrectly enunciated, which leads to a wrong statement even though the math is right.
The correct enunciations are:
1) "we can affirm the dice is not loaded".
2) The other must be the complement which is "We can not affirm that the dice is not loaded".
So the right statement are:
We're are 95% confident that the dice is not lodaded
We're not 99% confident that the dice is not lodaded
Great video!
Thank you very much for your video lecture. It is awesome and very useful. It would be even better if I can download your powerpoint/pdf. XDD I really appreciate your fantastic work! Thank you!
Awesome presentation
I'm a bit confused about one part: In the end, can we really say that we are 99% sure that we have the fair die? Because I thought that the hypothesis test is only about rejecting or not rejecting the null; that if we can't reject the null then all we can really say is that we can't reject it (based on the sample and p-value). There can still be quite a small probability that the observed variance occurred by chance. It's just that that probability isn't small enough for us to base rejecting the null hypothesis on. Basically, the difference between type I and type II errors, as I understand them. Please correct me if I'm wrong :)
Hello! You are correct. I think I addresses this in other comments. I was speaking colloquially there, easy to slip into when making videos on the fly. Unless we control type II error, we either reject or fail to reject the null.
Thanks for posting this video.
According to the CHI Square formula, the denominator was supposed to be the Hypothesized variance right? and here it seems like the expected value is taken as the denominator. Will somebody clarify this?
14:34 Dont be that guy in class!! :D Amazing video about chai** square test(pun intended)