Abstract
In order to quantitatively analyze information within a dataset or population, statistical sampling tools should be employed. The application of various applicable tools will allow researchers to derive conclusions pertaining to the dataset collected. Subsequently, Cluster Sampling, Stratified Sampling and Systemic Sampling are effective tools in statistics that may be extremely beneficial to researchers, when applied in the appropriate context.

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Sampling Methods in Statistics

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Introduction
When comparing attributes of two or more populations of samples or within a single population of samples, statistical analysis must be used. Subsequently, the filed of Statistics will employ hundred’s of various mathematic models and tools that will be specific to the dataset, and attribute that is being analyzed. Additionally, the particular statistical tool of choice must consider the conclusion that must be determined, from the dataset. This report will function to analyze three popular statistical techniques; Cluster Sampling, Stratified Sampling, and Systematic Sampling.

Cluster Sampling / Stratified Sampling / Systemic Sampling
Cluster Sampling is a popular method best employed when the target population represents a portion of the overall sampling population. The analyst is therefore attempting to derive a certain attribute about the population that is located within a larger group (Easton et.al, n.d. ). A real-Life application of cluster sampling analysis is observed when comparing the times of a group of athletes, for a 100 meters sprint before and after training (Easton et.al, n.d.). While this method may be used to qualitatively measure the effectiveness of a change to a population, stratified random sampling will allow for execution of alternative attribute specific measurements within a population.

This method will involve dividing a population into sampling groups (Strata) and a random sample will be obtained from each group. The obtained samples will then be grouped to form one random sample (Investopedia, n.d.). This method is best suited for investors of capital. Investors may quantify the risk of an ETF (which is composed of many smaller stocks), and assign a rating such as AAA+ or F-, as applicable. Finally, Systemic sampling stays true to the name. Samples may be obtained at defined periodic intervals during an event or occurrence (Easton et.al, n.d.). This method may be used to determine the overall composition or of a dataset, or to trend a flow. For example, when measuring the wind speed of a hurricane, researchers may obtain readings at one location in fixed intervals.

Conclusion
In conclusion, the particular attributes of ones data set and the information that one is attempting to derive will govern the selection of an appropriate method. While the tools described are useful in the context of their application, researchers will typically utilize various sampling techniques to demonstrate consistency across their particular dataset.

References
• Easton, V., & McColl, J. (n.d.). Statistics Glossary – sampling. Retrieved from http://www.stats.gla.ac.uk/steps/glossary/sampling.html