# Analyzing Sampling Populations, compare and contrast

Discuss how sampling populations are both similar to and different from populations. What is the single most significant difference? Why?
Discuss whether sampling errors can be eliminated altogether. How would you accomplish this if you could?
Discussion 2: “Reliability and Validity”Please respond to the following:

Does reliability or validity present more challenges to researchers? Why?
Provide an example of the validity of measuring instruments and the reliability of measuring instruments.
Discuss at least one way of making measuring instruments either more valid or more reliable. (Be as creative as possible!)

I see these types of sampling: simple random, stratified, cluster , and systematic sampling.

First question: What makes the populations the same and what makes them different. What makes them the same, is when you have a group of students, all learning the same things, hopefully, and that group can be a random sampling of people taking a test to gauge how people take tests. These samples show how test takers understand the logic of test taking, and what might make it either easier or harder for test sample takers and their makers.

Within that group, you have less control over what is being sampled for the entire group. Clusters can be found in this group, and stratified sampling may have less value. Stratified sampling takes in to account the same large group, but then the goal is different. In this type of sampling, a researcher or teacher is looking for a behavioral result based on how the test questions are answered. This type has less to do with who does best and who does worst and who is in the median group. So stratified sampling still takes data from the whole population being tested, but that data is not used to score competency in academic progress.

Cluster sampling is when only a specific group is being sampled based on the needs of state testing in schools, driving tests in states, end user groups like store customers. These are closed set samples where the sample is looking for data in which to perform a certain outcome. Cluster samples can be considered unfair, mainly when used in school testing, whereas the other groups either get the answers right and pas/fail, or a particular group within the cluster is being identified to better address the needs of that group within the cluster.

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