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KMID : 1101220020340020149
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2002 Volume.34 No. 2 p.149 ~ p.154
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A Study of Quality Control by Statistics
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Chang Sang-Wu
Kim Nam-Yong
Kim Jin-Gak
Hong Wung-Gi
Son Hyun-Ju
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Abstract
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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 values 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.
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KEYWORD
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Bias, Parameter, Random sampling, Target value, Standard deviation, Sample population
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