The conclusion you made at the end using the confidence interval......to reject/ fail to reject the H0 is so great.... it sharpens our thought process. Thanks Brandon...
Hello, Brandon. Thank you soooo much for these videos. I'm currently working on my master thesis and I had a huge lack of statistics knowledge. Yesterday I was feeling so anxious and desperate because I could not understand what to do with my data. Today, I feel so much better because these videos have helped me a lot, more than any statistics teacher. Thank you, thank you, thank you!!!
HI Nikita! This problem uses actual data from a real problem. We wouldn't create data from a random number generator. In this type of problem, each diet subject has two measurements, before and after; the data is a matched pair (same person). What we are analyzing is the mean difference between each pair.
11 ปีที่แล้ว +5
Hi, regarding the "Interval Estimate for Fight the Flab" slide, you say that the interval estimation is always two-sided, which is correct of course. Yet you are using the same 5% t-value (1.833) you used for the one-sided case. For a two-sided 95% confidence interval with 9 degrees of freedom, the t table reads 2.262 (the larger confidence interval does not contain zero either). Can you please clarify this?
Brandon - thank you for your videos they are excellent. I've been through statistics before but still some concepts I forget and these make it very easy to revise them. Like Joao below, I was very confused about the 95%CI calculation and couldn't understand why tcrit was the same for the one-tailed H0 test and the CI calculation which is 2 tailed. Unless I have failed to understand some point here, the tcrit for the 95%CI needs to be +/- 2.262. This works out as an interval of -48.44 to -5.96 pounds. Thanks for doing these videos. Will you be doing ones on the tests of significance of correlation and ones on non-linear regression also?
Hey Hop. I screwed it up, you are correct. Sometimes I try to go to fast, reuse slides to save time, and this happens. I will annotate and redo the video as soon as I can. My apologies.
Hi Brandon, Thanks for the video. I want to know why CI cannot be one-tailed, to follow suit the hypothesis tests? If CI is one-tailed, we can keep using -27.7 - 17.21 [from t-Critical Value = -1.833] Then we can ignore the range from Upper bound (-10.48 lbs) to the Sample Mean (-27.7 lbs). We only need to see the range between Sample Mean (-27.7 lbs) and the Lower bound (-44.91 lbs). Pls advise.
Hi, is there a reason why the formula you indicate at about 10:52 for difference is "before minus after", but the formula you then use is "after minus before"? I ask because "after minus before" makes more sense to me (what was the net change), but my textbook uses "before minus after". Both should work for testing a hypothesis, but if an online test asks a partial question (what is "d" or the sample mean of "d"), they might be looking for one or the other.
Hi, Brandon. Many thanks for a great work you have done. Statistics always attracted me somehow, but I never really understood all of the concepts to the extend I wanted to before I found your video-course. I am watching really carefully all of the series and I have one question regarding this particular video. Is that right that actually there are two different, but equally correct approaches how to do hypothesis test. a) find t(critical), find t(test) and see if t (test) is below or above t(critical) b) find CI for test data and see if hypothesized value falls in or out. I got a bit confused because it seems you you used both approaches in this video.
Why can't we compute the Variance of sample of differences = Var (w_b)/10 + Var (w_a)/10? This will be similar to how we calculated variances of the sampling distribution in earlier two population videos.
Dear Brandon Foltz, I'm a bit confused about why the difference between means in a two-sample test follows a normal distribution. could U help me answer this question?
Heyy I think there's some confusion. Ha says the weights are decreasing. We're measuring Wb - Wa. If weights are decreasing, then Wb - Wa will be positive. And hence, the alternative hypothesis should say that the mean of differences should be greater than 0. Or if the alternative hypothesis is to remain the same i.e. the mean of the differences is lesser than 0, then we should measure Wa - Wb. Then the differences will be negative and so will the mean. Doesn't that make sense? I could be totally wrong here, kindly correct me if you think so.
you're absolutely correct. I noticed that too. Either he would be using 'Before minus After' and get a null hypothesis of weight loss and a mean less than zero, or he could use the mean less than zero, except it would be After minus Before, not Before minus After.
hi brandon. In the example you are using, you assumed d bar follows a student's t-distribution with n=10. Is it because of central limit theorem? CLT states if n is large enough, sample mean of any variable follows a normal distribution, regardless the distribution of original variable. Here d bar is the sample mean of difference in weight with n = 10. Since n is small, we can assume d bar follows a student's t. Am I right on this one? Without CLT, we cannot tell the distribution of d bar. Please comment. Thanks!
Sorry the Cohen's d was -1.42 not 1.42. What is the range of values for Cohen's d? Is - wrong or simply telling me one value (i.e., mean is larger than the other?). Thanks so much Brandon, Tasch.
Technically, wouldn't it appear that we can't disprove that the diet doesn't work? Or am i taking things too far? Thank you for the very helpful videos!
Hi Brandon! Thank you so much for these videos. Great work! I had a question, do we need to calculate sample means using random number generators for each problem of this kind? I see you've followed the same example in this playlist, and we use the values of x/bar/1 and x/bar/2 as calculated in this video, for all the ten samples for each population.
Hi, Brandon, awesome video! Quick question that'll likely have a not so quick answer. Would you say analyzing longitudinal data is basically a multivariate version of Matched sample t tests? Also, do you have any videos or know of any good videos that cover longitudinal data in depth?? Thanks!
Hello Brandon. I have calculated a "-" value for my t-statistic. Is this then telling me that the important area is to the left of the mean? I checked the Cohen's d and got 1.42 (is this possible because I was of the understanding that values ranged from 0-1). Thanks Brandon. Tasch.
Earlier in the video you said we were using lower tail and we used the 1 tail value from the t-table, now that you are showing the interval estimate you are saying that it will be two tailed, next to the margin of error formula, so which is it please?
Nia Mukasa Hello! It's both. The one tail is for the null hypothesis test that the observed difference is greater than or equal to zero, which is the yellow dot. The confidence interval is the area extending left to right *from that dot.* That interval does not contain zero which reaffirms our conclusion in the hypothesis test. So both work in tandem.
thankyou Brandon .your video helps to understand everything i don't understand in statistics.i wanted to ask you about the degree of freedom of two tests,here yours is not the same i used to see in the books.df=n1+n2-2.can you explain me why
Hi Brandon, I really like your channel. Thanks for sharing it with us. I am looking for any resources which summarize everything all together (roadmap in statistics) and help to understand briefly which hypothesis test choose and when. Would you be able to suggest any good valuable resources?
Hi, Brandon. Thank you for your videos first, I've watched almost all of them. I am a little confused about the method to test the hypothesis. When should I set up a confidence interval and when should I compare t-test and t-critical？Thank you very much.
Are there any videos that differentiate when an ANOVA (and each variation), t test (and variations), and other tests should be used over the alternative options together rather than each one separate? These are super helpful, but just curious :)
Hello! Not sure what you mean here. But a one-way ANOVA would be used then there are three or more groups (means) you wish to compare. Pairwise t-tests compound error and should not be used. Hope that helps.
***** I guess what I am asking is this: my teacher gives us a data set in spss and basically just asks, you want to compare variable a with variable b, what test would you use? Knowing what each test measures is great, but how do you decide to use a t test over an anova? Then when looking at each type of t test or anova variations, what do you look at in spss to help decide?
+Amit Sood a test statistic(like a zscore) , its like taking a value(sample) and see where it falls in the normal(hypothosized) distribution. if its t/z/f value falls in alpha region than we dismis null, coz this value is so rare under normal distribution. our assumption must be wrong. hope it helps
Your lecture is very precious to code statistics program for myself. I found this problem is about One-tailed(Lower). so confidence interval -44.91 to -10.48 pounds is impossible How about below 49.1112? the weight difference above 0 is true, but above 49 is statistically untrue.
The conclusion you made at the end using the confidence interval......to reject/ fail to reject the H0 is so great.... it sharpens our thought process.
Thanks Brandon...
Hello, Brandon. Thank you soooo much for these videos. I'm currently working on my master thesis and I had a huge lack of statistics knowledge. Yesterday I was feeling so anxious and desperate because I could not understand what to do with my data. Today, I feel so much better because these videos have helped me a lot, more than any statistics teacher. Thank you, thank you, thank you!!!
I love how you always begin each video with an encouraging and reassuring message for us!!
That's greatly appreciated and heartfelt, honestly.
HI Nikita! This problem uses actual data from a real problem. We wouldn't create data from a random number generator. In this type of problem, each diet subject has two measurements, before and after; the data is a matched pair (same person). What we are analyzing is the mean difference between each pair.
Hi, regarding the "Interval Estimate for Fight the Flab" slide, you say that the interval estimation is always two-sided, which is correct of course. Yet you are using the same 5% t-value (1.833) you used for the one-sided case. For a two-sided 95% confidence interval with 9 degrees of freedom, the t table reads 2.262 (the larger confidence interval does not contain zero either). Can you please clarify this?
Your Series are so great and time tested. Thanks
Thank you SO MUCH for such logical explanations! Was struggling with CPA course, but your videos clarify so much! Thank you!
I have watched all of your videos. You sir, are amazing.
Thanks, clears the long standing "errors on self confidence" on this subject!
I went through all of your presentation. It was amazing, and great! Big Thank you!
Wonderfully explained. Thank you Brandon..
Brandon - thank you for your videos they are excellent. I've been through statistics before but still some concepts I forget and these make it very easy to revise them.
Like Joao below, I was very confused about the 95%CI calculation and couldn't understand why tcrit was the same for the one-tailed H0 test and the CI calculation which is 2 tailed. Unless I have failed to understand some point here, the tcrit for the 95%CI needs to be +/- 2.262. This works out as an interval of -48.44 to -5.96 pounds.
Thanks for doing these videos. Will you be doing ones on the tests of significance of correlation and ones on non-linear regression also?
Hey Hop. I screwed it up, you are correct. Sometimes I try to go to fast, reuse slides to save time, and this happens. I will annotate and redo the video as soon as I can. My apologies.
Annotated.
Hi Brandon,
Thanks for the video. I want to know why CI cannot be one-tailed, to follow suit the hypothesis tests?
If CI is one-tailed, we can keep using -27.7 - 17.21 [from t-Critical Value = -1.833]
Then we can ignore the range from Upper bound (-10.48 lbs) to the Sample Mean (-27.7 lbs).
We only need to see the range between Sample Mean (-27.7 lbs) and the Lower bound (-44.91 lbs).
Pls advise.
even i have the same doubt, why is the 95% CI using two tailed method instead of one
Mean of difference in alternate hypothesis should be positive because if there is weight loss then before-final will be positive
Again another masterpiece. Don't understand why these 9 guys didn't like the explanation.
I have to thank you for your videos. They have helped me a great deal.
Easy to follow! Very educational.
Nice tutorial, thank you very much! Can anyone tell me if we would always reject H0 if the confidence interval doesn't contain 0?
Thanks a lot , these videos are very good and improves confidence to learn more
Hi, is there a reason why the formula you indicate at about 10:52 for difference is "before minus after", but the formula you then use is "after minus before"? I ask because "after minus before" makes more sense to me (what was the net change), but my textbook uses "before minus after".
Both should work for testing a hypothesis, but if an online test asks a partial question (what is "d" or the sample mean of "d"), they might be looking for one or the other.
marin of error uses t(a/2) or t(a)? 19:40 uses t(a/2). the next slide use ta
Well, it says in brackets that it's [t(a/2)] for two tailed tests
Hi, Brandon. Many thanks for a great work you have done. Statistics always attracted me somehow, but I never really understood all of the concepts to the extend I wanted to before I found your video-course. I am watching really carefully all of the series and I have one question regarding this particular video. Is that right that actually there are two different, but equally correct approaches how to do hypothesis test. a) find t(critical), find t(test) and see if t (test) is below or above t(critical) b) find CI for test data and see if hypothesized value falls in or out. I got a bit confused because it seems you you used both approaches in this video.
Why can't we compute the Variance of sample of differences = Var (w_b)/10 + Var (w_a)/10? This will be similar to how we calculated variances of the sampling distribution in earlier two population videos.
for the interval estimate shouldnt the confidence interval be 2.2622 since its two tail
These are literally god sent
Dear Brandon Foltz, I'm a bit confused about why the difference between means in a two-sample test follows a normal distribution. could U help me answer this question?
Heyy I think there's some confusion. Ha says the weights are decreasing. We're measuring Wb - Wa. If weights are decreasing, then Wb - Wa will be positive. And hence, the alternative hypothesis should say that the mean of differences should be greater than 0.
Or if the alternative hypothesis is to remain the same i.e. the mean of the differences is lesser than 0, then we should measure Wa - Wb. Then the differences will be negative and so will the mean. Doesn't that make sense? I could be totally wrong here, kindly correct me if you think so.
you're absolutely correct. I noticed that too. Either he would be using 'Before minus After' and get a null hypothesis of weight loss and a mean less than zero, or he could use the mean less than zero, except it would be After minus Before, not Before minus After.
at 30:00 what do the variances mean? Since we are doing a matched pair test do they matter?
I love these videos. Do you have the "indexed" somewhere with links to each one?
you are the best thank you for your lectures
Have you ever considered showing how this paired t-test via SPSS?
-1.833 should be OK since it is a one-tailed test. In a one-tailed test we don’t divide the .05 by 2.
hi brandon. In the example you are using, you assumed d bar follows a student's t-distribution with n=10. Is it because of central limit theorem? CLT states if n is large enough, sample mean of any variable follows a normal distribution, regardless the distribution of original variable. Here d bar is the sample mean of difference in weight with n = 10. Since n is small, we can assume d bar follows a student's t. Am I right on this one? Without CLT, we cannot tell the distribution of d bar. Please comment. Thanks!
Sorry the Cohen's d was -1.42 not 1.42. What is the range of values for Cohen's d? Is - wrong or simply telling me one value (i.e., mean is larger than the other?).
Thanks so much Brandon, Tasch.
No, the only reason I'm watching this is because my lecturer sucks big time
Technically, wouldn't it appear that we can't disprove that the diet doesn't work? Or am i taking things too far? Thank you for the very helpful videos!
Hi Brandon! Thank you so much for these videos. Great work!
I had a question, do we need to calculate sample means using random number generators for each problem of this kind?
I see you've followed the same example in this playlist, and we use the values of x/bar/1 and x/bar/2 as calculated in this video, for all the ten samples for each population.
Excellent video
Hi, Brandon, awesome video! Quick question that'll likely have a not so quick answer. Would you say analyzing longitudinal data is basically a multivariate version of Matched sample t tests? Also, do you have any videos or know of any good videos that cover longitudinal data in depth?? Thanks!
Can I do a Two Populations, Matched Sample z-test, basically I not on sample but on the entire population? if yes, how?
This is great - I get it - thank you for making these great videos...
Can you report your t statistics in p value format. lets say it was p value > or < alpha value?
or converting my p value into t statistics versa versa
Hello Brandon. I have calculated a "-" value for my t-statistic. Is this then telling me that the important area is to the left of the mean? I checked the Cohen's d and got 1.42 (is this possible because I was of the understanding that values ranged from 0-1). Thanks Brandon. Tasch.
Earlier in the video you said we were using lower tail and we used the 1 tail value from the t-table, now that you are showing the interval estimate you are saying that it will be two tailed, next to the margin of error formula, so which is it please?
Nia Mukasa Hello! It's both. The one tail is for the null hypothesis test that the observed difference is greater than or equal to zero, which is the yellow dot. The confidence interval is the area extending left to right *from that dot.* That interval does not contain zero which reaffirms our conclusion in the hypothesis test. So both work in tandem.
ahh!! ok, thanks! Oh and I'm having trouble finding the video that shows how to use excel to find the values, which video is it please?
thankyou Brandon .your video helps to understand everything i don't understand in statistics.i wanted to ask you about the degree of freedom of two tests,here yours is not the same i used to see in the books.df=n1+n2-2.can you explain me why
Hi Brandon, I really like your channel. Thanks for sharing it with us. I am looking for any resources which summarize everything all together (roadmap in statistics) and help to understand briefly which hypothesis test choose and when. Would you be able to suggest any good valuable resources?
Hi, Brandon. Thank you for your videos first, I've watched almost all of them. I am a little confused about the method to test the hypothesis. When should I set up a confidence interval and when should I compare t-test and t-critical？Thank you very much.
shouldn't this be 1 population 2 dependent samples t-test?
I have an exam after a few hours, really I don't know how to thank you.
Awesome video!
Dang.. I really needed you to a step by step of Sd, that's what I am stuck on
Just excellent
This is great!!! Thanks a lot
Really great video! Thankyou so much :).
Pure gold..
Are there any videos that differentiate when an ANOVA (and each variation), t test (and variations), and other tests should be used over the alternative options together rather than each one separate? These are super helpful, but just curious :)
Hello! Not sure what you mean here. But a one-way ANOVA would be used then there are three or more groups (means) you wish to compare. Pairwise t-tests compound error and should not be used. Hope that helps.
***** I guess what I am asking is this: my teacher gives us a data set in spss and basically just asks, you want to compare variable a with variable b, what test would you use? Knowing what each test measures is great, but how do you decide to use a t test over an anova? Then when looking at each type of t test or anova variations, what do you look at in spss to help decide?
Two variables use the t test. More than two ANOVA.
***** I was obviously over thinking it lol thank you! I shared your videos with my class :) they love them
Awesome thank you! You all can do it. Just keep working at it.
master piece!
Sor I'm really impressed by you , thanks alot
You really saved my life!!!!!!! Thank you for all of your awesome videos!!!
good vid
Thank you
Got confused, What is a test statistics. Isn't test statistics = margin of error?.
+Amit Sood a test statistic(like a zscore) , its like taking a value(sample) and see where it falls in the normal(hypothosized) distribution. if its t/z/f value falls in alpha region than we dismis null, coz this value is so rare under normal distribution. our assumption must be wrong. hope it helps
Your lecture is very precious to code statistics program for myself.
I found this problem is about One-tailed(Lower).
so confidence interval -44.91 to -10.48 pounds is impossible
How about below 49.1112? the weight difference above 0 is true, but above 49 is statistically untrue.
Very very confused as to difference between Matched Sample t-test and 2 population t-test.
stats KING
There's only one population, the difference, that's why the formula's are the same as 1 sample. BAMM! :D::DD ;D