0:00:00.290,0:00:05.114
As we briefly showed before, when finding the probability of rolling an average
0:00:05.114,0:00:09.650
of at least three with the tetrahedral die, the central limit theorem is not
0:00:09.650,0:00:14.402
only awesome, but important, because it allows us to know where any sample mean
0:00:14.402,0:00:19.842
falls on the distribution of sample means. In the example of the tetrahedral
0:00:19.842,0:00:25.482
die, we wanted to know the probability of getting at least a three, for an
0:00:25.482,0:00:31.037
average, if we rolled it twice. And we found that when we looked at the
0:00:31.037,0:00:36.400
histogram, rolling at least a 3 was 6 out of 16. And now, we're extending this
0:00:36.400,0:00:42.290
concept to populations. So, if we have the distribution of sample means where
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the samples can be any size. Where does a particular sample mean of that same
0:00:47.580,0:00:52.948
size fall on the distribution? If we know where it falls on the distribution,
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then we can decide if this sample is typical or if something weird is going on.
0:00:58.140,0:01:00.633
So, let's use another example.