A quick introduction to Stratified Random Sampling and Cluster Sampling.
2 thoughts on “Stratified Random Sampling and Cluster Sampling Presentation”
Thank your for sharing.
The advantage of random sampling is that it does not bias the experimental subjects. Usually, the experimental subjects are a large group. Random sampling is randomly selected in a large group, so the probability of everyone being selected is the same. This creates a balanced subset that most likely represents the entire larger group.
Researchers will use a lot of resources in experiments. Cluster sampling selects certain groups from the entire population. This process reduces the investment of resources and money. Dividing cluster sampling into homogeneous groups increases the feasibility of sampling. For each cluster, the entire group is represented, so more subjects can be included in the study.
Thank your for sharing.
The advantage of random sampling is that it does not bias the experimental subjects. Usually, the experimental subjects are a large group. Random sampling is randomly selected in a large group, so the probability of everyone being selected is the same. This creates a balanced subset that most likely represents the entire larger group.
Researchers will use a lot of resources in experiments. Cluster sampling selects certain groups from the entire population. This process reduces the investment of resources and money. Dividing cluster sampling into homogeneous groups increases the feasibility of sampling. For each cluster, the entire group is represented, so more subjects can be included in the study.
Thanks for the presentation as this has helped me when trying to figure out some of the samples needed for my topic.