## THIS USER ASKED ðŸ‘‡

**Explain the difference between a stratified sample and a cluster sample. (select all that apply.) in a cluster sample, the only samples possible are those including every kth item from the random starting position. in a cluster sample, every sample of size n has an equal chance of being included. in a stratified sample, random samples from each strata are included. in a stratified sample, the only samples possible are those including every kth item from the random starting position. in a cluster sample, the clusters to be included are selected at random and then all members of each selected cluster are included. in a stratified sample, every sample of size n has an equal chance of being included. in a cluster sample, random samples from each strata are included. in a stratified sample, the clusters to be included are selected at random and then all members of each selected cluster are included.**

## THIS IS THE BEST ANSWER ðŸ‘‡

In a cluster sample, each sample has the same chance of being included. In a stratified sample, random samples from all strata are included. In a cluster example, the clusters to be counted are selected at random and then all members of each selected cluster are included. In a stratified sample, each sample has the same chance of being included

Step by step explanation:

In a stratified example the population is divided into different parts and then we take random elements from each segment.

In a cluster sample, the sample is divided into parts (or clusters) and then the sample is taken by selecting different clusters.

Thus, in the cluster sample we take ALL elements from different clusters and in a stratified sample we take SOME elements from the different sections.

Now let’s look at the options given:

In a cluster example, the only possible examples are those including all kth items from the random initial location: FALSE. In the cluster example we select each item from the cluster. In a cluster sample, each sample has the same chance of being included: TRUE. if we divide the sample into clusters of size n then each cluster has the same chance of being selected (since we select them at random). In a stratified sample, random samples from all strata are included: TRUE. We have already said that we will take a random sample from each segment (strata). In a stratified example, the only possible examples are those that include all kth items from the random initial location: FALSE. We can apply different methods to select our sample from each strata. In a cluster example, the clusters to be counted are selected at random and then all members of each selected cluster are included. TRUE. This is the definition of an example cluster we first wrote. In a stratified sample, each sample has the same chance of being included: TRUE, we take samples from elements, not from stratum. In a cluster sample, random samples from all strata are included: FALSE. This is the definition of a stratified example. In a stratified example, the clusters to be counted are selected at random and then all members of each selected cluster are included: FALSE. This is the definition of an example cluster.