Credit: Stebastiaan Ter Berg CC-BY-SA 2.0
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# What is the importance of statistical quality control?

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Statistical quality control is important because it uses statistical methods to monitor the quality of a product. This type of auditing maximizes manufacturing productivity and minimizes errors associated with human judgement.

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Statistical quality control relies on statistics and data reports to assess quality and is therefore an efficient means to evaluate a manufactured product. Statistical quality control helps maintain the consistency of how a product is made. Statistical quality control methods can include cause-and-effect analysis, check/tally sheets, histograms, Pareto and scatter analyses, data stratification, defect maps, events logs, progress centers and randomization. Graphical charts and graphs, the part of statistical process control that monitors the manufacturing process, help decipher the statistics and data from quality control reports. One important method of statistical quality control is acceptance sampling. In acceptance sampling, a sample of a product is randomly taken to determine whether or not to continue making the product. If the percentage of "good" or acceptable product is higher than "bad" or defective product in the sample, the product is approved as is and manufacturing continues. If not, the product is rejected and production stops. Any evaluation process that involves samples and human judgement usually assumes a margin of error. With statistical quality control, human error is reduced.

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## Related Questions

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Statistical significance shows the mathematical probability that a relationship between two or more variables exists, while practical significance refers to relationships between variables with real-world applications, according to California State University, Long Beach. Two or more variables do not need statistical significance to have practical significance, and vice versa.

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The main steps utilized in a statistical investigation include four components: clarifying the problem and formulating questions or hypotheses that can be answered with the data, designing or creating an appropriate experiment that can collect the data required, finding and using the appropriate techniques needed to accurately analyze the collected data, and interpreting the collected data and results so as to answer the questions and hypotheses that were proposed in the first place. The main steps in a statistical investigation are thought to be a cylindrical process that helps ensure that all procedures are completed logically. The aim of opening a statistical investigation is to answer the many questions that are present in the world, and is a technique that has been commonly applied by statisticians.

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A good statistical sample size is at least 100, and preferably more, participants. With a 100-participant sample size, the researcher has a margin of error of approximately 10 percent with a 95 percent confidence rating in the results. For the researcher to increase his confidence rating and reduce his margin of error he has to increase the size of the sample.