00:01
Hello everyone, so let's do the statistics problem, but before i get started, i recommend that you do the question yourself and come back to see if you got it right or not.
00:14
So hopefully you'll tell the question yourself, now we can work on together.
00:18
So we're given these questions and we're given these multiple choice options and you have to choose one.
00:26
So the first part is talking about a distribution of sample means, it's a collection of means from and we have to fill or finish that sentence.
00:35
So let's think about it.
00:38
To create a sampling distribution, a researcher must select a random sample of a specific size, referent from the population, because it would be difficult to get a sample or more expensive and time costly if we choose everyone from the population, so we would have to pick a sample from that population of a certain size.
01:05
And then we would have to calculate the sample mean from that and plot the sample mean on a frequency distribution.
01:12
And repeat this steps in infinite number of times until we're complete with our sample or satisfied with our research.
01:20
So this tells us that a distribution of sample means is a collection of means from samples of size and from the same population.
01:30
It's not going to be different sizes.
01:33
That would make our research invalid because.
01:37
Because it's just not fair to pick different samples of different sizes.
01:42
They would all have to be the same to make it easier for us to make a comparison between all of them as well.
01:48
So they can remove the different sizes.
01:50
We can eliminate that in a way.
01:52
And then samples of size and so we have the same population or different population.
01:57
If we use different population, that again also makes our research invalid or inaccurate because we might have different demographics or whatever it is there's going to be differences in the people with different populations so it's just also going to be very expensive and just a lot of waste of time if we look at different samples from different populations easy to just pick a sample or samples from the same population and be able to make the comparison so this is the answer for the first next we have to reduce the variability of estimates from a simple random sample you should the reason, so let's think about it.
02:42
So if we want to reduce variability, we have to use a larger sample size, right? because when there's a larger sample size, there's more people.
02:53
So there would be a greater difference between all of them.
02:58
So it would make us, it would be better for our research.
03:02
So if we increase bias, obviously that is no account.
03:05
We'll use variability, right? that's just going to make our results inaccurate.
03:08
Use a count and not a percent.
03:10
That does not really affect variability in general.
03:13
So then we're going to be left for these two.
03:17
So larger sample or a smaller sample.
03:19
A sample is just going to increase our variability of the estimates, make it harder for us and more inaccurate for the results.
03:26
So a larger sample is always better.
03:29
So we have the law of large number says that, well, let's think about it.
03:35
So let's say we have an instrument.
03:40
Of a sample mean of size n, it tends to be closer and closer to the population mean when n gets closer to infinity.
03:50
So that is that our law of large number.
03:53
So we can determine from that that the law of large number says that the sample mean becomes a better approximation of the population mean as the sample size grows...