00:01
Once again, welcome to new problem.
00:05
This time we're dealing with hypothesis testing.
00:09
We're dealing with hypothesis testing and when it comes to hypothesis testing, we could always have hypothesis testing for means and for the means we're dealing with one sample t test.
00:23
We're looking at the one sample t test and the test statistic for the one sample t test is x bar minus.
00:31
Minus mu not, oliver s over radical n.
00:36
Remember, mu not is the hypothesized, this is the hypothesized population mean and n is the sample size.
00:54
Of course, when we look at s, this is the sample standard deviation.
01:03
So s becomes the sample standard deviation and not forgetting x bar, which is the sample mean.
01:15
And so we have a new problem, and in this particular problem we're looking at, assume the number of people selected is 40.
01:28
So we have 40 people selected.
01:30
And the accuracy, the level of accuracy of their wristwatches checked.
01:37
So sometimes the wristwatches happen to be ahead of the correct time.
01:43
So we call that positive errors.
01:46
And then other times the wristwatches happen to be behind the correct time.
01:51
And those ones we call them negative errors.
01:53
The mean of this distribution is 99 seconds and the standard deviation.
02:00
Is 221 seconds.
02:03
And so we're going to use those numbers to determine at alpha of 0 .01, whether or not the population of all watches have a mean of zero seconds.
02:16
So the first thing is we set up the now and alternative hypothesis...