Some Important Notes for Theory of Sampling
Sampling is a method of proceeding with collecting information by considering a small sub-group amidst a large population. The conclusions done for the sample in research can be equated to the entire population, as the sample is a representative of the population. The sampling methods could vary depending on the size of the sample, the type of research taken, the purpose of the research or the sampling technique necessary. Based on the tools used for collection of data, for example, interview schedule, or questionnaire, analysis of information in the form of finding average, performing statistical analysis like Analysis of Variance are all dependent on the sampling techniques followed by the researcher. The authenticity of the information collected from samples help in the Theory of Sampling to a great extent. Usually Distribution in the form of Normal or Poisson is used to analyze the data and form effective conclusions.
What are Difficulties Encountered In Theory of Sampling Problems?
Whatever be the method of sampling, sometimes Theory of Sampling will not deftly go in accordance with the actual values. The students might find several discrepancies in the data obtained even if they had followed the same sampling techniques. This might be due to researcher errors that might creep in the Theory of Sampling. The variance in the samples obtained should be as less as possible however; there are chances that students may find it arduous to adhere to the low variance as they might not get exact values similar to true values. Bias, which is the difference between the obtained value and true values, might creep in either due to system error, researcher error, data entry error, where values entered might mismatch with the actual numbers, bringing in mistakes, which might change the judgment on the data collected, leading to misinterpretations.
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