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RISS 인기검색어
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- 저자 : 홍웅기 ( Wung Gi Hong ) (검색결과 1 건)
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장상우 ( Sang Wu Chang ),김남용 ( Nam Yong Kim ),김진각 ( Jin Gak Kim ),홍웅기 ( Wung Gi Hong ),손현주 ( Hyun Ju Son ) 대한임상검사과학회 2002 대한임상검사과학회지(KJCLS) Vol.34 No.2
Statistical inference is the use of a probability theory to make inferences about a population from sample data. Suppose we want to estimate the characteristics of a population such as the target value (monthly average mean) in a laboratory. We obtained data from a sample and used the results to make inferences about the population. A sample population is drawn from a lot number made by manufactures, and collection of all subjects or objects of interest. A sample is a subset of the population used to make inferences about the characteristics of the population. A population parameter is a numerical characteristic of a population, a fixed and usually unknown quantity. Data are xalues measured or recorded on the sample. Sample statistics are numerical characteristics of the sample data such as the mean, CV, SD, proportion or variance. It can be used to provide estimates of the corresponding population parameters. Different samples give different values for sample statistics. By taking many different samples and calculating a sample statistics for each sample (e.g. the sample mean) , you could then draw a histogram of all sample means. A statistic from a sample or randomized experiment can be regarded as a random variable and the histogram is an approximation to its probability distribution. The term sampling distribution is used to describe this distribution, i.e. how the statistic (regarded as a random variable) varies if random samples are repeatedly taken from the population. Bias is distance between the parameter and expected value of sample statistics. If the sampling distribution is known then the ability of the sample statistics to estimate the corresponding population parameter can be determined. In particular, the sampling distribution determines the expected value and variance of the sampling statistics. If the expected value of the statistics is equal to the population parameter, the estimator is unbiased. If the variance of the statistics is ’small’ and it is also unbiased then an observed statistic is likely to be close to the population parameter. Random sampling is a sampling technique where we select a group of subjects (a sample) for study from a larger group (a population). Each individual is chosen entirely by chance and each member of the population has a known, but possibly non-equal, chance of being included in the sample. Variation in a process can be defmed as the amount values from about the average value for a given population. It has been demonstrated that variation in a stable process will vaη randomly about the process`` average value within the process’ capabilities if no outside force is acting upon it. The goal of the total error program should be to minimize and eliminate if possible these variations and to operate a process such that it has the capabi1ity to produce a product within a customers`` specification or better. When variations from outside forces are eliminated, all variations left in the process are inherent to the process and the process is performing at its optimum. In conclusion, one of the best ways is using a target value and standard deviation from statistics in a monthly population that draws samples in order to minimize total error in a clinical laboratory because insert statistics where a vendor is supplied is a veη large variation and it is easy to interpret it. We are sure that the latest data from a monthly population are providing the best information for quality control and reducing bias.
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