What is the difference between a population and a sample?What is the difference between a population and a sample? How large do you think your population and sample will need to be for your dissertation research?.

#### What is the difference between a population and a sample?What is the difference between a population and a sample? How large do you think your population and sample will need to be for your dissertation research?

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Re:Module 5 DQ 2

Focus on Research: LISA Mcelwee

What is the difference between a population and a sample? How large do you think your population and sample will need to be for your dissertation research? Explain your reasoning. What factors other than size will you consider in relation to sampling? Why? What types of descriptive statistics will you consider for summarizing the sample? Why?

A researcher must identify a population in which the study is based upon investigating, while the sample is the portion that represents the population which data is collected; for example, a study could investigate a topic involving the graduating class at a local University which is a total of 425 students, this would be considered the population but through the random selection process only 250 of those students are slated to participate, representing the sample.

Researchers must determine the sample size as this is an important step because not only does the sample represent the number of participants that are to be studied from the population, but it should also be comparative to the goals of the study; Lenth (2001) states the sample size should be large enough but not too large to be considered of scientific significance. The sample size is important as it influences how accurately the sample represents its population (Gravetter & Wallnau, 2013); because it directs the error variance also known as the standard error. Therefore, the larger the sample size means the outcomes should be more accurate than with a smaller sample size; which Gravetter & Wallnau (2013) calls the law of large numbers. In other words as the sample size increases the error between the sample and population means decreases. For example in a study that has a sample size of 4 could result in a standard error of 5 while a sample size of 100 decreases the standard error to 1.

Descriptive and inferential statistics can be used during analysis, especially when groups of people are studied according to Gravetter & Wallnau (2013); descriptive for organizing and summarizing raw scores into something more manageable, while inferential is the method which using sample data to make generalizations about the population.

Although this learner’s dissertation idea is still work in progress, it is this learner’s desire to have a large enough population that will equate to a sufficient sample size to decrease the standard error.

References:

Gravetter, F. J. & Wallnau, L. B. (2013). Statistics for the Behavioral Sciences (9th ed.). Belmont, CA: Wadsworth, Cengage Learning.

Lenth, R. V. (2001). Some Practical Guidelines for Effective Sample Size Determination. The American Statistician, 55(3). Retrieved from http://conium.org/~maccoun/PP279_Lenth.pdf.

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