That's a brilliant, quick, simple explanation of that statistical test. I have never seen someone explaining statistics so clearly, wow!! Thanks a lot, was really helpful
Wish I found these sooner. I can just see the process at a small scale for a few minutes instead of just having an equation plopped down at me. Thank you sir
Thank you so much for sharing these videos by hand. This has been immensely helpful for my epidemiology preparation this year! I really appreciate your work.
Thank you professor. By a very big mistake we followed old outline for statistics and just found out a week ago. Our finals are in less than a week. So now we are covering up our mistake. This helped a lot since fortunately only few test were not same in both outline (old and new). So this video have actually helped quite a lot. Once again. Thank you.
Thank you so much for taking the time to do these videos... I'm in graduate school doing epi/biostats and these videos just saved my academic career. You explain in a way that is very easy to understand, thank you!
Thank you SO MUCH!! This was incredibly helpful as my Stats final is this coming week, and the Mann-Whintney was great too - I wish I found you MUCH sooner. Thanks!
Dear Professor Eugene, I believe you meant the difference between the measurements of aluminium content in months of august and november for beau pré (beautiful meadow) is 6.2, not 6.3, please...
You are a lifesaver. I'm not even in university anymore, but I only had one statistics course and it wasn't helpful for applying statistics to real-world data. I at least understand what I'm looking at and what I need thanks to you.
Really lovely video! Thank you. Seems that the GIBECQ difference should equal 7.3 not 5.3 and the BEAUPRE difference should equal 6.2 not 6.3. This subtly changes the rank orders. Seems that would lower T_- from 16 to 15 and increase T_+ from 75 to 76, but doesn't change the finding.
I believe you have made a miscalculation at 4:06. Both the difference and the absolute difference between the November and August data is 6.2, not 6.3.
Normality requirement for individual samples is not needed for the paired-sample t-test, only the difference b/w sample data must be normally distributed
hello, thanks for the nice video. I have a question: what if two paired data sets are not normally distributed but the sample size is big (say 500 samples)? It looks like almost every table displays the critical value for 30 or 50 samples max.
I tried (unsuccessfully) following another method that used the sign function sgn(x) to complete this test. Do you have a tutorial for that? How do you derive the critical values when the critical value table is not given?
Professor thanks so much. I have been lost in class this video is so helpful. But please could you explain how to know when it's either a one tailed or two tailed test
Eugene, thank you for this. Two questions for you: Let’s say I have T+ of 9 and T- is -16. Is it correct to say you would then use T+ as 9 is lower than the absolute value of T-? Let’s say I have T+ of 52 and T- of 0. Do I use T- as my test statistic? Also if in wanted to calculate the effect size, I read I should use the absolute test statistic and divide by the sqrt of n, but if it’s zero, this will just result in zero… even though there was a big positive swing. Hope this makes sense, nice to hear an Irish voice here away from home.
I am confused, I think we can perform the paired T-test here since the data is normally distributed according to the Shapiro test with a P-value of 0.304. I think the assumption of normality in the paired t-test for the difference between the matched-pairs, not for the data sets in general.
Thanks for the clear explanation. Is the assumption about symmetry of the difference distribution necessary for the sample to qualify as a candidate for the signed-rank test? In your example, the difference sample has a skewed empirical distribution, though (skewness = 1.0004) - so I am wondering if it works without the symmetry assumption stated here - www.stat.umn.edu/geyer/old03/5102/notes/rank.pdf.
Thanks a lot for your video sir. I have a question though : shouldn't the normality test be performed on the difference only, instead of both november and august data?
Hi Manon, You can test the differences for normality if you wish. However, quite often one set of data might be normal, but the second isn't. It is useful to know if your data is normal or not. Dr E.
This was great! I think I could do this now! Dear Professor, what should you do if your second set of data (i.e. November) is "smaller" than your original data (e.g. some data was lost). Or in other words, your ending sample size is smaller? Can this test still be used? Thank you.
Hi Allison, The Wilcoxon Signed Rank test is a paired test - both samples need to be the same size. If you have unpaired data that is not normally distributed you could use the Mann-Whitney U Test. Dr E.
Hi Nurul, In a two-tail test (as in my video) you are only looking to find if there is a significant difference between the two sets of data. You are not concerned if one is greater or less than the other. However, if you are looking to find if November is greater or less than August, the Null Hypothesis would be different. This is therefore a one-tail test and you would report results differently. Hope this helps, Dr E.
Your video is very helpful! Just one question, how do we rank the absolute value of differences like -1 and 1 since they both become 1 after turning them into absolute values?
