A stratified sample is a group of individuals or groups of individuals who are selected at random. This is important because the individuals in a stratified sample may not be representative of the overall population.
In a cluster sample, the individuals are selected based on some criteria. A classic example is when you’re at a football game and all the people are under the age of 25.
A stratified sample may be a sample that’s taken randomly or a stratified sample that has been selected based on some criteria. A typical stratified sample would be when you’re picking five people from a crowd to guess the names of the popular members of a rock band. The stratified sample is designed to be representative of the population.
The most prominent example of a stratified sample is when scientists take a group of people and select a random selection of them to be studied (for example, to develop a blood test). Sometimes it is done to eliminate the effect of other people in the study. Such research is called a cluster sample because the people in the sample are not randomly selected.
In other words, stratified samples are usually a good approximation of the population, but there are different types. When a selection is made of a larger group, the results of the study may be affected by the larger group, as well as by other individuals in the larger group that are not sampled.
You should also take into account the types of sampling you can use in order to make a cluster sample. If you only take into account the sample’s characteristics, it may be easier to find out which members of the same group are in different clusters.
Let’s say you’re studying the differences between the sexes. It’s pretty easy to use this to figure out which cluster the study was in – women are more likely to be in the same cluster, and men are more likely to be in a different cluster.
you can only really tell whether a sample is stratified or not if you look at it as a whole. If you have a larger sample and look at your characteristics as a whole, it wont be clustered. The same goes for a sample.
I think it’s best to think of a cluster sample as the one that is most easily defined by your general characteristics. This is also the point where you can tell whether a sample is a stratified sample. Think of a cluster sample as having a few characteristics that are very useful to you, and a lot of others that are less useful.
Cluster samples are a good way to look at your data. I know when I first started looking at my data, I was a little bit surprised by how many different ways people can describe themselves. For example, I have a lot of friends who are all shy and introverted. While I would never describe myself as shy, I also know a lot of people that are shy and introverted.