00:01
Okay, and your question says, which of the following statements best defines statistical significance? now, one of these options is the best statements to define that, and the other ones describe in a way how we try to find out if there is statistical significance, not necessarily what statistical significance is.
00:24
For example, if you look at option two, it states the difference between the observed results and the hypothesis results is large.
00:35
Having a large difference by itself is not enough to prove statistical significance, but it's usually a first indicator we look at to try to measure the probability of that difference occurring.
00:49
So that's just explaining or trying to talk about one of the ways we first start to look for statistical significance.
00:58
So it's out, that's not what statistical significance is.
01:04
The next one says the results are at least as extreme as the observed results if the null hypothesis is true.
01:12
So what we do when we do hypothesis testing is we calculate, we assume the null hypothesis is true, then we have an alternative hypothesis that represents our test results, and we see how, what the probability of those things occurring would be if the null hypothesis is true.
01:33
If we get a very low probability that they would occur on their own, if the null is true, then we reject the null.
01:40
But again, this whole option, option three, is talking about ways we identify statistical significance, not what it is.
01:51
The next one mentions there is less than 5 % probability...