Simple Random Sampling - Essay Example

This method would allow collection of data in all the target areas without any bias. Simple random sampling is a statistical tool used in research. In this research method, a subset of a sample is chosen from a population. All individuals are chosen randomly by chance, so that all individuals have the same probability at all the stages of the sampling process. (Patton, 2002). In both large and small populations, such process is done without replacement. An individual avoids choosing a member of a population more than once.

Simple random can also be done with replacements but, when done without replacement, it not independent but it still accommodates interchangeability. For an extremely small subset from large population, simple random sampling without replacement gives the same results as wit replacement. Unbiased sampling is incredibly paramount so that the sample selected represents the entire population. This does not show that the sample is a perfect representation of the population, but it provides valid conclusion in relation to the population.

This is the simplest sampling technique, and it requires a complete sampling frame which is not feasible to construct from a big population. (Zikmund, 2010). One critical advantage is that it is free from errors, and it requires exceptionally little knowledge on the subsets. The respondents were people who could understand all that were required, and thus especially minimal explanation was required from the researchers. The questions that were used were applicable to all the businesses that were involved in the research.

This is because, in simple random sampling, the subsets selected are picked from the target population. This makes it easy to interpret the data collected. The data collected was easy to analyze, and analyze since the respondents had almost the same answers to the research questions. The method was also suitable because the research was not gathering technical information that required experts. It was economical to explain the questions to the respondents, since it did target a large number of people.

(Creswell, 2009). On the contrary, it has a lot of errors due to the randomness of selection that can give samples that do not reflect the characteristics of the actual population. For example, a simple random sampling of 20 businesses will averagely produce 10 businesses that got assistance from venture capital and 10 that did not, but the actual trial will over present or under present one of the category. The result of the research was incredibly consistent with the expectation of the researcher.

There was no immense variation between the actual and the expected. This implies that simple random was a superior method for this research. Simple random sampling is tedious and exceptionally cumbersome when done from a large population. There was a lot of movement from one enterprise to the other to look for the respondents. Some of the respondents were not available at the time of conducting the interview, and so the researcher had to go to another time. There were especially many entrepreneurs to be interviewed, and this method was so effective since it saved time.

This was so imperative since it will not be feasible to interview all the businesspersons. It also complicated if the research questions are exceedingly many. The questionnaire had many questions, and this method only allowed the target group to answer the questions. The