00:01
Okay, so let's go through these different adjustment methods.
00:04
So the bonferroni adjustment is that the adjusted significance level is equal to the original significance level divided by the number of tests.
00:15
Now we have 10 different tests here.
00:17
The original significance level is 0 .1.
00:19
So this is just going to be 0 .1 divided by 10, which is 0 .01.
00:23
And so that is the significance level for all tests using the bonferroni correction.
00:28
The home adjustment method is to say that the significance level for test i, if you, so firstly you rank the p -values in order, so the smallest would be 1 and the largest would be 10, etc.
00:53
Then the significance level for the p -value which is in the i -th place in that ranking system is equal to the original significance level divided by the total number of tests plus one minus the rank.
01:11
And i'll show you what that is for all of the different p -values in a moment.
01:17
And finally, the benjamin -hockberg adjustment.
01:23
In order to do that, we find that the significance level, again, we rank the p -values in order like we did in the holm adjustment.
01:30
And the significance level for the i -th one, the one in the i -th position, is equal to the rank of that one divided by the total number of tests times alpha...