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The visual learner statistic page includes 5 types of sampling. Cluster sampling, simple random sampling, stratified sampling, systematic sampling, and convince sampling. Cluster sampling is a sampling plan used when a population is sectioned into groups and then one of those groups is selected as the sample and every person in this group is part of that sample. It is often used in marketing research. In this sampling plan, the total population is divided into these groups and a simple random sample of the groups is selected. An example is seen when trying to find out the average income tax of Oregon. It would be too difficult to get a random sample of Oregon so the researcher could use cluster sampling to get data from certain cities in Oregon, and that would give them an idea of income tax in Oregon. Simple random sampling is seen when individuals in a population are selected in a way that each person has the same chance of being chosen. Random sampling is a subset of individuals randomly selected by researchers to represent an entire group as a whole; it would be used at a school to find out student satisfaction. Stratified sampling is one that ensures that subgroups of a given population are each adequately represented within the whole sample population of a research study. This would be used to try and sample student’s grade point averages throughout the U.S. Systematic sampling is an ordered selection of individuals of a group, starting with the first and then going with the 5th or 10th for example, and continuing that order. This can be seen with every 7th person leaving a restaurant receiving a survey on the food and service. Convenience sampling is when one person interviews the people in their given area. An example of this can be seen in a classroom, asking how the students would rate their teacher.

Reference

The Visual Learner: Statistics. 2018. Retrieved from https://lc.gcumedia.com/hlt362v/the-visual-learner/terms.html.