The sampling distribution method is a statistics application that distributes data of all possible samples from the same population of a given size. Sampling distribution is essential for inferential statistics, and it is vital when making inferences over the overall population. A survey is done and repeated on all possible samples of the population to get significant results. I believe that this method is most efficient as it gives the most accurate results when conducting a survey.
I would use this method when surveying the number of working-class college students in our district area. I would first apply the target population in the area, which will be young adults aged 18-30. I would then group this population into samples according to their place of hangout. For instance, the college library, the youth church, food courts within the college premises, and the sports area.
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After identifying my samples, I would collect data via face to face recorded interviews and prepare open-ended questionnaires for the students who are busy or too shy for an interview. I would repeat these procedures for all the selected sample groups. This method will ensure that I get maximum information to draw my conclusion. When collecting the data, I would include basic questions like name, age, gender, and marital status. I would also ask whether or not these individuals were working and request them to state their reasons. I would try to find out how working affects their studies and if there is preferential treatment for the college's working students. The successful collection of data will enable me to calculate the standard deviation and mean to draw a provable conclusion. This method might prove to be time-consuming, but the results will be most accurate. Sampling distribution might not give the same answers for every sample tested. The results with the highest frequency during sampling distribution are the considered ones.
