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

Statistical Hypotheses: There are two types of statistical hypotheses to include null and alternative hypothesis. Null Hypothesis - Samples observations resulting purely by chance. Alternative Hypothesis - Sample observations influenced by non-random causes.

In example, when determining the end result in a coin flip. A null hypothesis may result in half Heads and half in Tails. However, the alternative hypothesis may reflect differently with the number of Heads and the number of Tails received. In conclusion, if the number of flips totaled 100 times, resulting in 60 Heads and 40 Tails, we would determine the null hypothesis would be rejected and not of use. The coin would be deemed as not fair nor balanced.

Blocking - Is the act of grouping experimental units into block segments with comparable characteristics that may influence the end result of the experiment. The design with both control and randomization, are taken into consideration (Easton, Valerie J 2014).

In example, a study being carried out has a total of 100 subjects. The plan is to divide the subjects into random, yet equally split groups of four.

Census - Is data collected from every individual within a population and calculated to assist with achieving statistical information.

An example of a census is the United States Census. It is distinct from the United States Census of Agriculture.

Cluster Sampling - Is often used when natural groupings are apparent. Populations are divided into clusters.

In example, common clusters are created geographically. Unlike the census sampling, clusters can be separated by residents on each individual street within each individual city. Cluster analysis is appropriate when clusters