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ductor plays a major role in the determination of a sample size when there are items readily available or convenient to collect. However, there are more scientific ways to estimate a sample size. For instance, the experimenter could use a target for the power of the statistical test to be applied once the same is collected or he/she could use a confidence level which determines how accurate a result will turn out with lower chances of error. The requirement of a good sample is that the estimation should be based on these scientific forms and the means. One of those ways is using the standard error of the sample mean, that achievement with the corresponding formula: σ/√n , where n is the sample size and the σ2 the corresponding variance In addition to this, we express the 95% of the confidence interval with the form: (x̅-2σ/√n, x̅+2σ/√n), where x̅ is the sample mean with a Normal Distribution and defined using the Central Limit Theorem. Therefore, if we wish to have a confidence interval that is W units in width, we should calculate: n = 16σ2/W2. This means that the smaller in range we need the interval to be then the bigger the size of the sample. For example, if we are interested in estimating the amount by which a drug lowers a subject’s blood pressure with a confidence interval that is ten units wide, and we know that the standard deviation of blood pressure in the population is 20, then the required sample size is 64, [see 4, “Sample Size Determination”, para. 7]. A special case of the means is the estimation of a proportion. The estimator of a proportion is p̂=X/n, where X is the number of ‘positive’ observations. The correspondent formula for this case is: (p̂-2√(0.25/n), p̂+2√(0.25/n)) for the confidence interval. Consequently, if we wish to have a confidence interval that is W units in width, we should calculate: n = 4/W2 = 1/B2, where B is the error bound on the estimate. For instance, for B=10% then the required sample size is 100. This special case is often used for opinion polls, [see 4, “Sample Size Determination”, para. 6]. Source of errors There are many potential types of errors in survey sampling. According to Groves (1989)[see 1], the survey errors can be divided into two major groups: First, the errors of nonobservation where the sampled elements use only part of the target population, and the second one is the errors of observation, where the listed data deviate from the truth. Some examples of errors of nonobservation can be ascribed to sampling, coverage or nonresponse which is going to be analysed in the later part of this report. On the other hand, examples of errors of observation can be attributed to the interviewer, respondent or method of data collection. Both of our sources of obdurate errors can vigorously affect the accuracy of a survey. However, these errors cannot be eliminated from a survey but their effects can be reduced by caref

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