We do not use a t-test for a one-sample test for proportion. We use the z distribution. I teach undergraduate and graduate statistics and have reviewed a dozen statistics textbooks and with 99% confidence, claim that we do not use the Student's t-distribution when testing a proportion. From the Stats Stackexchange: "The reason you can use a z-test with proportion data is because the standard deviation of a proportion is a function of the proportion itself. Thus, once you have estimated the proportion in your sample, you don't have an extra source of uncertainty that you have to take into account." Z distribution doesn't ask for sample size to determine the critical value, whereas t distribution does. However, as sample size gets large, z and t converge until t = z. For example, the sample size of 1000 would have no difference between z and t values.
The z-test for proportions is DERIVED from the binomial distribution under the assumption of a large sample size. The key idea behind this test is based on the Central Limit Theorem, which states that the distribution of the sample mean of a large enough sample will be approximately normally distributed, regardless of the shape of the underlying population distribution. hence you use z-test with proportion data
I took the data analytics course on Coursera and they also taught to use only a z-test for proportions. Rushed to the comments for confirmation so thank you lol
There is nothing wrong with the 1-sample z test. It produces the correct answers as long as you have the population parameters. But because we rarely have the population parameters we don't use it. Its not inherently flawed as implied by the host of the video. It's pretty easy to explain why it isn't used very often, like I just did. Otherwise, great video.
I was going to say the same thing. In addition, when the sample is greater than 30, both tests are pretty much the same. For the Chi-square, he should have said that each "bin" needs to have more than five observations. Two of them were below five.
@@Canuck1000do you know the difference of 2 sample t-test proportion vs chi squared? I feel they are quite interchangeable, like the depression example he mentioned. Can it be used for chi-squared as well?
@@claireli5044 The issue that is discussed here is the z-test vs t-test (population vs sample). The z-test is valid if we know the information about the entire population, but is very difficult to obtain as Z3r0 said above. It should not be automatically rejected. In the end, if n>30, both tests will give you the same results (if it is a sample still use the t-test though).
@@claireli5044 Hi, isn't the t-test for comparing the means of two or more different continuous variables and the chi-squared test for nominal and/or ordinal variables? If I am not mistaken I don't think they are interchangeable. I would appreciate it if someone can correct my understanding. Thank you.
I am from the university of Jos, Plateau State, Nigeria and l have never learned well during the masters degree statistics classes l took for one year+ but you make it look simple. You are a good communicator and l feel more confident about my statistics knowledge because of you.
@@joed2444 ohh I see. I should add then that this video really only tells you things to memorize about these topics. If you dive deeper into the mathematics behind every statistical test, you will not need to remember what test to apply in which situation, it will just make sense. Just my two cents if in case you are interested in studying stats.
I’m a mechanical engineer. Of course we took the appropriate statistics and probability class but never saw the real significance until my 5th year of being an engineer and working at GE healthcare. Now I apply the DMAIC approach as much as needed.
You are damn right, buddy! I had such a similar experience, and I believe to many out there! I found that the DMAIC thing of LSS is so interesting. So, you are practising it within the healthcare industry? Wow, that is great!. Maybe we can catch up for more experience sharing...😊
Simply the greatest explanation. I spent hours trying to get the main gist and difference of all these confusing test. This video was the brilliant saver for me. Thank you so much !!!!!
You use the Z-test when you know something about the population standard deviation. This doesn't happen very often, so the t-test is more common. Saying you should never use it, or that it's "bad" or "dumb" or "unprofessional" is just NOT accurate. It's just more RARELY used. Throwing it out shows a lack of understanding.
I’m not statistician but I’ve been in the statistics classes at least all three or four you might take during your undergraduate year and when you learn about those particular test they also tell you when you should use them. That’s part of learning about the actual test. I think that’s where we’re having a breakdown in the education process now statistics is more college level but even on a more basic level we might learn how to do math but we don’t learn the principles behind the mathematics you should learn the principles behind the statistics tests then you know when to use them
This was super helpful in so many levels. Not only did you described the tests in detail without hassle but it helped me understand when to use them. Thanks!!
Thank you very very much I have an exam tomorrow and you explained it clearly that I understand now I really appreciate your efforts you are a genius thank youuuuuuuuuuuuuuuuuuuuuu
Kody, Thank you so much. I have spent days trying to figure out ANOVA versus Chi Square versus T tests and so on, and you made it so easy. I am really grateful --and also a little baffled that so many other sources make it so complex.
Thanks so much for sharing the knowledge... for FREE! However, one of my statistics teachers, used to say to me to use t-test for a small sample size i.e., of less than 30 with unknown standard deviation of a population with a normal distribution property. That explanation still holds?
Thank you so much! I wish every statistics class started this way. Getting to look at the bigger picture first and then jumping into details is always a better way to learn things.
Ok, first, I love the way you present the information = terrific!!! 2nd soooo easy to understand; you speak in everyday language. thank you so much for this video, I got it!!
Hello, This lecture has helped me to understand using T and ANOVA Test on categorical variable. Previous my thought was T and ANOVA is used only for MEAN difference.
Hopefully, I can gain a better understanding of this topic. My 2nd take with Probability and Statistics for my undergraduate degree in Psychology (Science) via Online.
I think proportions are for categorical response or variables and not qualitative as said here, qualitative research itself its so much complicated with thematic or content analysis of codes and quotes, regarding Z and t test I think its about sample size that dictates which one to be used, I stand to be corrected if I am wrong
Z test for proportions T test for means Chi Squared Goodness of Fit for 1 sample with 1 variable to test if it is different for the population (ie test if the distribution of race is different in 1975 population to the 1980 sample) Chi Squared Independence for 1 sample with 2 variables to test if they are independent (ie test if chess and IQ scores are associated) Chi Squared Homogeneity for 2 samples with 1 variables to see if they have the same distribution (ie to test if men and women have the same distribution of living arrangements) T test for slope
Hi Sir, i need your help. From below info, what you understand. Can you explain to me, pls? Hypothesis i) There is a positive relationship between salary and employee retention - BETA VALUE (-0.379), Pearson Correlation (-0.289) Result : Accepted ii) There is a positive relationship between communication and employee retention - BETA Value (-0.159), Pearson Correlation (0.110), Result (Accepted) iii) There is a positive relationship between job satisfaction and employee retention which impact their decision to stay : BETA Value (-0.115), Pearson Correlation (-0.136), Result (Rejected)
Talk about learning Math 10 Statistics class Online, and then a Research Methods class again online. Now I'm good with online, but not online for harder classes.
When he talked about the 2-independent sample test for mean, 2-independent sample test for proportion, and paired sample test, are those all t-tests just with different null and alternative hypotheses? Or are there different ways to do those tests other than the t statistic?
We do not use a t-test for a one-sample test for proportion. We use the z distribution. I teach undergraduate and graduate statistics and have reviewed a dozen statistics textbooks and with 99% confidence, claim that we do not use the Student's t-distribution when testing a proportion. From the Stats Stackexchange: "The reason you can use a z-test with proportion data is because the standard deviation of a proportion is a function of the proportion itself. Thus, once you have estimated the proportion in your sample, you don't have an extra source of uncertainty that you have to take into account." Z distribution doesn't ask for sample size to determine the critical value, whereas t distribution does. However, as sample size gets large, z and t converge until t = z. For example, the sample size of 1000 would have no difference between z and t values.
Thank you for the correction 🙂
The z-test for proportions is DERIVED from the binomial distribution under the assumption of a large sample size. The key idea behind this test is based on the Central Limit Theorem, which states that the distribution of the sample mean of a large enough sample will be approximately normally distributed, regardless of the shape of the underlying population distribution. hence you use z-test with proportion data
ok tryhard
@@beachwave5705that’s such a bananas thing to say to a professor making a correction to a statistics video on RUclips 💀
I took the data analytics course on Coursera and they also taught to use only a z-test for proportions. Rushed to the comments for confirmation so thank you lol
There is nothing wrong with the 1-sample z test. It produces the correct answers as long as you have the population parameters. But because we rarely have the population parameters we don't use it. Its not inherently flawed as implied by the host of the video. It's pretty easy to explain why it isn't used very often, like I just did. Otherwise, great video.
I was going to say the same thing. In addition, when the sample is greater than 30, both tests are pretty much the same. For the Chi-square, he should have said that each "bin" needs to have more than five observations. Two of them were below five.
@@Canuck1000 he seems to be quite incompetent
@@Canuck1000do you know the difference of 2 sample t-test proportion vs chi squared? I feel they are quite interchangeable, like the depression example he mentioned. Can it be used for chi-squared as well?
@@claireli5044 The issue that is discussed here is the z-test vs t-test (population vs sample). The z-test is valid if we know the information about the entire population, but is very difficult to obtain as Z3r0 said above. It should not be automatically rejected. In the end, if n>30, both tests will give you the same results (if it is a sample still use the t-test though).
@@claireli5044 Hi, isn't the t-test for comparing the means of two or more different continuous variables and the chi-squared test for nominal and/or ordinal variables? If I am not mistaken I don't think they are interchangeable. I would appreciate it if someone can correct my understanding. Thank you.
I am from the university of Jos, Plateau State, Nigeria and l have never learned well during the masters degree statistics classes l took for one year+ but you make it look simple. You are a good communicator and l feel more confident about my statistics knowledge because of you.
🇳🇬❤
I’m on a PhD level program with a statistics course and this is incredibly useful sir. You deserve 22 million subscribers!
Same as here
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no offense but how is any of the information in this video useful to you while doing a phd in statistics? isn't this taught in high school statistics?
@@prithvidhyani2002 Maybe in your high school. Some of us are still learning the basics at the graduate level.
@@joed2444 ohh I see. I should add then that this video really only tells you things to memorize about these topics. If you dive deeper into the mathematics behind every statistical test, you will not need to remember what test to apply in which situation, it will just make sense. Just my two cents if in case you are interested in studying stats.
saved me so much time researching the appropriate technique I should use to test my hypothesis. Glad i watched it
Spent 16 weeks in a graduate level stats class, but learned everything I needed to know in this 20 minute video. Thanks! Super clear.
Came here to say the same thing! lol
yes, agree :)
Those 16 weeks has laid a huge foundation for u to understand this 20' lecture easily though.
Right? I was thinking in class, there has to be a more concise way to explain this stuff.
Given its a year later, you've probably now also learned that none of these tests are relevant any longer.
I’m a mechanical engineer. Of course we took the appropriate statistics and probability class but never saw the real significance until my 5th year of being an engineer and working at GE healthcare. Now I apply the DMAIC approach as much as needed.
You are damn right, buddy! I had such a similar experience, and I believe to many out there!
I found that the DMAIC thing of LSS is so interesting. So, you are practising it within the healthcare industry? Wow, that is great!.
Maybe we can catch up for more experience sharing...😊
Your reaction on the thumbnail is definitely me.
Simply the greatest explanation. I spent hours trying to get the main gist and difference of all these confusing test. This video was the brilliant saver for me. Thank you so much !!!!!
You use the Z-test when you know something about the population standard deviation. This doesn't happen very often, so the t-test is more common. Saying you should never use it, or that it's "bad" or "dumb" or "unprofessional" is just NOT accurate. It's just more RARELY used. Throwing it out shows a lack of understanding.
I'm another doc student who just learned some new things! Thanks!
Dude I spent $70,000 on a PhD in Boston and could have saved every penny had I seen this before. Thanks your fantastic.
I’m not statistician but I’ve been in the statistics classes at least all three or four you might take during your undergraduate year and when you learn about those particular test they also tell you when you should use them. That’s part of learning about the actual test. I think that’s where we’re having a breakdown in the education process now statistics is more college level but even on a more basic level we might learn how to do math but we don’t learn the principles behind the mathematics you should learn the principles behind the statistics tests then you know when to use them
This was super helpful in so many levels. Not only did you described the tests in detail without hassle but it helped me understand when to use them. Thanks!!
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Thank you very very much I have an exam tomorrow and you explained it clearly that I understand now I really appreciate your efforts you are a genius thank youuuuuuuuuuuuuuuuuuuuuu
Clear explanation, good example, energetic lectures! Thanks, this is so helpful!
If this video existed when I was in Uni I would definitely have made a better grade. 😭 May the good love save us from crappy lecturers
Where have you been all my statistics life? WOW!! Just brilliant...thank you so much.
Wow. Great introduction. The context is great. But apart from the context, the teaching style is fabulous. Thank you.
This is by far the best approach I've watched on youtube. And I've watched many. So, straight sub and like for sure.
Thank you! You explain this so much better than my professor does.
Kody, Thank you so much. I have spent days trying to figure out ANOVA versus Chi Square versus T tests and so on, and you made it so easy. I am really grateful --and also a little baffled that so many other sources make it so complex.
Thanks so much for sharing the knowledge... for FREE!
However, one of my statistics teachers, used to say to me to use t-test for a small sample size i.e., of less than 30 with unknown standard deviation of a population with a normal distribution property. That explanation still holds?
You are one in a million! I finally understand when to use Regression! Thank you & subscribed!
Thank you so much. My statistics semester packaged in your 20-minute video.
The energy you bring to lectures is contagious. The explanation is extremely clear. Great stuff. Please keep it coming. Thanks
omg have an exam on Tuesday and this was a lifesaver!! thank you!!
there is no equivalent t-test for one sample proportion due to the binomial approximation in these cases so only z -test for one proportion samples
Good
Thank you.
Thank you so much!
I wish every statistics class started this way. Getting to look at the bigger picture first and then jumping into details is always a better way to learn things.
That really helped me understand the differences. I've always struggled with that before, so thank you!
I commend you! This is the most comprehensive comparison with explanation so far. Thanks
Ery good explanation
You made it very simple thanks
Ok, first, I love the way you present the information = terrific!!! 2nd soooo easy to understand; you speak in everyday language. thank you so much for this video, I got it!!
What a clear explanation with examples. I could understand without statistic background. Thank you
Hello, This lecture has helped me to understand using T and ANOVA Test on categorical variable. Previous my thought was T and ANOVA is used only for MEAN difference.
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thank you so much for finding time to really explain this to us
Before i even finish this video , THANK YOU!!
Bro i cannot thank you enough. You explained it very clearly for me. Tq boss
Ugh, hated stats in grad school. Both rounds.
Am fascinated by stats now!!
LOVED THIS!!! NEVER UNDERSTOOD STATS BETTER
The anguish in thumbnail was basically me before watching this video
Thanks for posting this video. Clarified the difference between the tests
Hopefully, I can gain a better understanding of this topic. My 2nd take with Probability and Statistics for my undergraduate degree in Psychology (Science) via Online.
Very helpful. Thank you.
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Brilliant! You saved our time. Thank you!
Watching this two days before an exam 💀💀💀
Thank you! I'm in graduate school now and your videos still help!
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The best video on this topic. Thanks a lot
Great video of lecture of this young man! Thank you so much!
Great video, thanks for the clear explanations!
WOW thank you very much for that easy to understand explanation
Very well explained! THis video is super useful! THank you very much!!!
That was a surprise, the way the presenter was talking and the music at the start made me think I was watching an ad haha.
I think proportions are for categorical response or variables and not qualitative as said here, qualitative research itself its so much complicated with thematic or content analysis of codes and quotes, regarding Z and t test I think its about sample size that dictates which one to be used, I stand to be corrected if I am wrong
Thank you! This is so helpful for my understanding!! Wish my lecturers would watch this and summed up as such.
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Learned more in this video than my whole semester of research methods 😂
Z test for proportions
T test for means
Chi Squared Goodness of Fit for 1 sample with 1 variable to test if it is different for the population (ie test if the distribution of race is different in 1975 population to the 1980 sample)
Chi Squared Independence for 1 sample with 2 variables to test if they are independent (ie test if chess and IQ scores are associated)
Chi Squared Homogeneity for 2 samples with 1 variables to see if they have the same distribution (ie to test if men and women have the same distribution of living arrangements)
T test for slope
Thanks for making it so simple
Wow.. so well explained, Thanks a bunch Kody!
Thanks for this lecture! You are doing great!
Hi Sir, i need your help. From below info, what you understand. Can you explain to me, pls?
Hypothesis
i) There is a positive relationship between salary and employee retention - BETA VALUE (-0.379), Pearson Correlation (-0.289) Result : Accepted
ii) There is a positive relationship between communication and employee retention - BETA Value (-0.159), Pearson Correlation (0.110), Result (Accepted)
iii) There is a positive relationship between job satisfaction and employee retention which
impact their decision to stay : BETA Value (-0.115), Pearson Correlation (-0.136), Result (Rejected)
thank u somuch, you have made this so easy for me, i was really struggling. you really simplified it. thanks alot again :)
visit #herbalistehi for review and to be cured too no matter the virus or disease
Thanks Kody buddy!
Kody, THANK U!
Very helpful and clear video.
awesome, clearly defined to understand :) thank you!
Thank you! Very clear and exactly what I needed!
Great, this video was very helpful to me.
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Great explanation. Thank you 🙏
Clear & Simple - thank you for the summary!
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So simplified. amazing 🤩
Straightforward.
wow! thanks.
Talk about learning Math 10 Statistics class Online, and then a Research Methods class again online. Now I'm good with online, but not online for harder classes.
When he talked about the 2-independent sample test for mean, 2-independent sample test for proportion, and paired sample test, are those all t-tests just with different null and alternative hypotheses? Or are there different ways to do those tests other than the t statistic?
Interesting and easy to understand.
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I learned so much from this. Thanks!
Great video
Please clarify what type of test one can use if studies are going on journalism and media comparisons
Good explanation
Thank you so much for this!
Thank you! Very helpful video!!!
Thank you
It was really helpful❤
Amazingly explained 👏🙏🏻
Kindly give some explanation about ONE-WAY ANOVA test ...why we use this test ? Amour Learning
Amazing!! Thank you!
Great content! Thank you for sharing.
Thank you very much.
Thank you so much for making it so simple and easy to understand! You’re a lifesaver🙏🏾😭
Great video! Well explained!
Thanks man! great lecture
He's secretly the god of statistics
Thankyou. It was a great help.
Very very helpful thank you!!
It helped ....thank u so much
thank YOU
Thank you for the video
You are a genius!! Thanks for your video!!
THANK YOU FOR MAKING THIS.
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It was Perfect
You made statistics make sense