00:01
Hello guys and welcome.
00:02
Today i'm going to be explaining how to do this question, which asks us a type of probability sampling and provide an example of how you could use these.
00:10
I'm going to talk about the strengths and weaknesses.
00:13
So i'm going to pick cluster random sampling.
00:18
So one example of how you could use this to create a research study is you could go and pick, say, five groups of 28 people and a location.
00:30
Actually, location doesn't matter.
00:31
You can pick five.
00:32
Groups of 20 people.
00:34
And then you could test them for adhd.
00:37
Now this would create essentially a smaller population where you could test for adhd.
00:46
So let's see here, each cluster is a mini representation of the entire population.
00:51
This source is called corporate finance institute .com.
00:57
Now one advantage of cluster sampling is that it requires fewer resources, which means since it's faster, and it requires very resources, it would be more convenient to test.
01:09
Another advantage is that it's more feasible, which means that because each cluster is supposed to represent an attack population, more such as can be included in the study.
01:23
Now, a couple disadvantages of cluster sampling is that it's more biased.
01:28
Because this method is not as one with simple random sampling, it is more prone to biases, such as like, here, let me search of types of bias statistics.
01:46
Okay, i think the most prominent type of bias here would be selection bias, and this site is called h .tps, column, towards data science .com.
01:59
Yeah, so this is your high sampling error...