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Random sampling is a sampling where everyone is chosen entirely by chance and each person of the population has an equal probability of being included in the sample. It is the only way to make sure that the sample is not biased and is representative of the population. Probability sampling allows every facet of the study population to be represented without researcher bias (Grove, 2007). By using random sampling, there is a less chance of the results of the analysis being skewed. However, there are problems with random samples. Random sampling requires people within the population to volunteer to give out information. Those individuals who usually participate tend to have the strongest opinions in the topic being analyzed. Disadvantages of Random Sampling is biased results: This is one of the major disadvantages of random sampling. For instance, an interviewer searching to forecast will be succeed the next vote might just survey a region closer to them and take for granted the reason that the area is situated in the southern portion of the US and so will possibly have a further conservative slant. Individual prejudices might also creep into the information, as an assessor might not spread the questionnaires to specific group of people. These aspects often lead to twisted information gathering, rendering the information not valuable for monitoring trends all through the whole population.
Reference:
Cipher,D., Grove, S.(2007). Statistics for Nursing Research A workbook for Evidence-Based Practice. Elsevier
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