# Explain each sampling technique discussed in the “Visual Learner: Statistics” in your own words, and give examples of when each technique would be appropriate

Explain each sampling technique discussed in the “Visual Learner: Statistics” in your own words, and give examples of when each technique would be appropriate

The visual learner statistics learner statistics include five different types of sampling techniques. They are simple random sampling, systematic sampling, convenience sampling, stratified sampling and cluster sampling.

Simple Random Sampling: In a simple random sampling, individuals are chosen at random and not more than once to prevent a bias that would negatively affect the validity of the result of the experiment. Individuals in the sample are given equal opportunity to be selected.

Stratified Sampling: The population is divided into two or more groups called strata that share common characteristics and deriving a sample from each different group. Example: Collecting the same set of data from college educated individuals, individuals with some college education, and data from individuals with no college experience.

Convenience sampling: Sampling a group of people that are readily available, an easy or convenient way to collect a sample. Example: Collecting a data on a group of patients that have an appointment with their doctor in a single day

Cluster sampling: Is a simple technique where the entire population is divided into groups or clusters and then a random sample of these clusters are selected. Example of use of this type of sampling would be to find out about city taxes in Maryland. It would be too difficult to get a random sample in the state of Maryland, so the researcher could use cluster sampling to get data from certain cities in Maryland and that would basically give them an idea of city taxes in Maryland.

Systematic sampling: In systematic sampling, the member of the population is selected randomly at a particular starting number. Example a researcher has a total population of 100 individuals. From the list, the researcher randomly selects the first sample elements from the K elements of the population list, thereafter every K element on the list is selected.