00:01
We're looking at a hypothesis test.
00:02
The claim is that more than 30 % of freshmen at this university start with more than 20 credit accounts.
00:08
So the claim is that p, the population proportion of freshmen who start with this, is more than 30%, or 0 .3.
00:18
The matching hypothesis would be that's not true, it's 30 % or lower.
00:22
These represent the null and alternative hypothesis, hypothesis, respectively.
00:27
I know which is which because the null hypothesis always gets that equal sign.
00:32
Now, we've been given some information.
00:34
We have the sample size, we have the number in the sample criteria, but they've also given us the information we directly need.
00:41
The test statistic, 0 .74, and the p -value, 0 .231.
00:49
Now, a p -value is the probability of this result if the null hypothesis is true.
01:00
So you assume the null hypothesis is true, you find the probability of, in this case, such a high sample proportion.
01:08
If this probability is pretty high, you can say, okay, so in this case, 27 out of 80, that's more than 30%, but maybe it was just down to chance.
01:17
This wasn't unlikely...