00:01
We have some good questions about hypothesis testing here.
00:05
So in hypothesis testing, the probability of being wrong when it rejects the hypothesis is known as the p value.
00:14
Because it's that the p value is like the probability that what you're seeing is there given the null is true.
00:24
It's like a pretty low probability.
00:26
So that's what this is the p value.
00:29
It's a probability of seeing what you're seeing given the null is true.
00:32
So it's low, but you could reject it on.
00:35
You shouldn't.
00:36
That happens.
00:38
But that's what we're doing statistics for.
00:41
To make good judgments.
00:45
The statement in which you say there's a difference between two or more groups and some characteristics is called a research or alternative hypothesis.
00:56
Because your null is where, i mean, just for simplicity's sake, we'll say it's the means of one and two are the same.
01:07
And heck, you could have a test where they have three that are the same.
01:12
A lot of times it's two, but let's just say, we'll keep it as two.
01:16
Then the null would be that they're not equal.
01:22
I mean, you could have it be more or less than, but we're specifically talking about, is there a difference, not equal? three, if we reject null hypothesis, which is true, we have made a type 1 alpha error.
01:36
That's very much related to this p value, because the p value is the probability of seeing what we saw where the alpha is like the, what we're allowing to see.
01:56
Like, we'll allow like as low as a 5 % p value or 1 % p value or 0 .01 or something like that.
02:07
Which is the following is ordinal.
02:09
So attendance at religious services.
02:12
Let's see.
02:12
So a number of times attended last year...