**In the conclusion, I accidently put the word "sample" instead of "population." The hypothesis is in relation to the population but we calculate based off the sample.
Thank you for the question. The method is slightly different because the groups being analyzed have a difference in variance. The two sample t test assumes both samples have the same variance, that assumption is not made here.
Thank you for the question. The Welch's T-Test uses the formula in the video to get the Degrees of Freedom. I wish it was n-1, it would make calculating it easier. Let me know if you have any other questions.
Hello, can I ask if my question said ‘population variance is depend on the sample’, is it mean that I need to assume the two sample population variance are not equal? And I can use Welch’s t test?
Yes, when you think the variance of population 1 is different from population 2. Their respective samples will almost always not be equal. That is to say samples will almost always have variance with each other even if both are from the same population. As one of the assumptions for Welsh's T, you assume the two populations have different variances before sampling.
I am not all that familiar with Time Series but In terms of applying the T-Test for comparison I think I found something that may help you. The best response talks about what to do when you have the same or different lengths in time series. I didn't want to get credit for it when I personally didn't know it at the time. www.researchgate.net/post/how_do_we_measure_the_similarity_between_two_time_series_depending_on_magnitude
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Sorry, can you tell me where I make a mistake: In other formulas for the DF for Welch-test, I can see that in the denominator (s1^2/n1) and (s2^2/n2) are squared before they are divided by n-1. My result for DF is 25.532, very close to 25.409, but different.
Did you divide by n1-1 for the first group and n2-1 for the second? Rounding, your DF would be 26 and influence the critical value although the conclusion would be the same given how high the T is.
@@seanokonsky5096 , sorry for the late reply. Here is a link to a discussion on this topic. stats.stackexchange.com/questions/48636/are-the-degrees-of-freedom-for-welchs-test-always-less-than-the-df-of-the-poole It is called the Satterthaite-Welch adjustment.
@@mullvinayak You are correct. I did forget to square it in the video. I will make a comment correction. The DF ends up being 366 with a critical value of 1.99. The same conclusion for the test but an important step I missed. Thank you for bringing it up to me.
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**In the conclusion, I accidently put the word "sample" instead of "population." The hypothesis is in relation to the population but we calculate based off the sample.
You just saved my as* by showing me how i messed up two papers im working on. I am both annoyed and thankfull at the same time.
No problem!
I used the test here to see who might win today's fight between Usyk and Fury.
ruclips.net/video/r-3HBB3EKX0/видео.html
Why degrees of freedom of t test in unequal standard deviation is different?
Thank you for the question. The method is slightly different because the groups being analyzed have a difference in variance. The two sample t test assumes both samples have the same variance, that assumption is not made here.
For the Welch's T-test the degrees of freedom are the smallest n-1 right. So your degrees of freedom should be 15-1 = 14. Correct?
Thank you for the question. The Welch's T-Test uses the formula in the video to get the Degrees of Freedom. I wish it was n-1, it would make calculating it easier.
Let me know if you have any other questions.
Welch's t-test can be used to identify whether there is a significant difference between the two sets of data, but how do we interpret the results?
That is shown at the last minute of the video.
Hello, can I ask if my question said ‘population variance is depend on the sample’, is it mean that I need to assume the two sample population variance are not equal? And I can use Welch’s t test?
Yes, when you think the variance of population 1 is different from population 2.
Their respective samples will almost always not be equal. That is to say samples will almost always have variance with each other even if both are from the same population.
As one of the assumptions for Welsh's T, you assume the two populations have different variances before sampling.
@@Learn2Stats thank you so much for your reply
Thanks - any thoughts on the correct test to the bull of two time series of the same length are similar. If both modeled by an arima process ?
I am not all that familiar with Time Series but In terms of applying the T-Test for comparison I think I found something that may help you. The best response talks about what to do when you have the same or different lengths in time series. I didn't want to get credit for it when I personally didn't know it at the time.
www.researchgate.net/post/how_do_we_measure_the_similarity_between_two_time_series_depending_on_magnitude
Sorry, can you tell me where I make a mistake: In other formulas for the DF for Welch-test, I can see that in the denominator (s1^2/n1) and (s2^2/n2) are squared before they are divided by n-1. My result for DF is 25.532, very close to 25.409, but different.
Did you divide by n1-1 for the first group and n2-1 for the second?
Rounding, your DF would be 26 and influence the critical value although the conclusion would be the same given how high the T is.
@@Learn2Stats I also am wondering why your formula for DF seems to be different than other formulas I have seen online
@@seanokonsky5096 , sorry for the late reply.
Here is a link to a discussion on this topic.
stats.stackexchange.com/questions/48636/are-the-degrees-of-freedom-for-welchs-test-always-less-than-the-df-of-the-poole
It is called the Satterthaite-Welch adjustment.
@@Learn2Stats As compared to the Satterthaite-Welch adjustment, you forgot the squares in the denominator in the video.
@@mullvinayak You are correct.
I did forget to square it in the video. I will make a comment correction.
The DF ends up being 366
with a critical value of 1.99.
The same conclusion for the test but an important step I missed.
Thank you for bringing it up to me.