00:01
Once again, welcome to a new problem.
00:05
This time we're dealing with population parameters and sample statistics.
00:15
So we want to see what the difference between these two.
00:18
So if you have a sample and the sample is randomized, then what tends to happen is that a randomized sample, produces unbiased estimators.
00:39
So randomized sample produces unbiased estimators.
00:44
And the lack of bias means that each subject, each subject within the sample, within the sample, has an equal, has an equal.
01:07
Equal chance has an equal chance of being selected.
01:16
So there's an equal chance at selection.
01:20
And this is the hallmark of a randomized sample.
01:25
As long as it has an equal chance of being selected, it's going to be randomized.
01:32
So you're going to have a random sample.
01:35
X bar is, of course, a sample statistic.
01:42
It's a sample statistic and it's representative of the sample mean.
01:49
So that's x.
01:50
But we do also have mew, which is the population parameter.
02:03
So mew is the population parameter.
02:04
So mew is the population parameter.
02:06
And this is equivalent to a population.
02:11
Mean.
02:12
So that's an example of a population mean.
02:15
We're looking at a new problem.
02:18
And in this particular problem, we have two issues we want to deal with.
02:23
The first one is we want to describe what an unbiased estimator is...