attribute sampling

简明释义

特性抽样

英英释义

例句

1.The research team applied attribute sampling 属性抽样 to gather data on customer satisfaction levels.

研究团队应用属性抽样 attribute sampling收集客户满意度的数据。

2.Using attribute sampling 属性抽样, we can efficiently evaluate the performance of our suppliers.

通过使用属性抽样 attribute sampling，我们可以有效评估供应商的表现。

3.In the context of clinical trials, attribute sampling 属性抽样 helps in identifying adverse effects of a medication.

在临床试验中，属性抽样 attribute sampling有助于识别药物的不良反应。

4.The auditor recommended attribute sampling 属性抽样 to assess the accuracy of financial statements.

审计师建议使用属性抽样 attribute sampling来评估财务报表的准确性。

5.In quality control, we often use attribute sampling 属性抽样 to determine the number of defective items in a batch.

在质量控制中，我们经常使用属性抽样 attribute sampling来确定一批中的缺陷品数量。

作文

In the realm of quality control and statistical analysis, the concept of attribute sampling plays a pivotal role. It is a method used to determine whether a batch of items meets specified quality standards based on certain attributes. Unlike variable sampling, which measures the degree of a characteristic, attribute sampling focuses on pass/fail criteria or the presence/absence of a particular attribute. This makes it particularly useful in scenarios where the quality characteristics are binary in nature, such as defective or non-defective items. The process of attribute sampling begins with defining the population from which samples will be drawn. This could range from manufactured goods to service outputs. Once the population is established, a sample size must be determined. The sample size is critical because it influences the reliability of the conclusions drawn from the sampling process. A larger sample size typically yields more reliable results, but it also requires more resources and time.After selecting the sample size, the next step in attribute sampling is to randomly select items from the population. Random selection is crucial because it helps eliminate bias, ensuring that every item has an equal chance of being included in the sample. This randomness enhances the validity of the findings, making them more generalizable to the entire population.Once the samples are collected, they are tested for the specific attributes defined at the beginning of the process. For instance, if a company manufactures light bulbs, the attribute might be whether each bulb functions correctly or not. Each bulb is then classified as either 'acceptable' or 'defective.' The results of this classification can provide valuable insights into the overall quality of the production batch.The outcome of attribute sampling is usually expressed in terms of proportions or percentages. For example, if out of 100 sampled light bulbs, 5 are found to be defective, the defect rate would be 5%. This information can then be used to make informed decisions regarding the quality of the entire batch. If the defect rate exceeds acceptable limits, corrective actions can be taken to improve the production process.One of the significant advantages of attribute sampling is its simplicity. The method is straightforward and easy to implement, making it accessible for various industries. Moreover, it provides quick results, allowing organizations to respond promptly to quality issues. However, it is essential to recognize that attribute sampling has limitations. Because it only considers whether an item meets a criterion, it does not provide information about the degree of quality. For instance, it cannot distinguish between a light bulb that barely meets the quality standard and one that far exceeds it. In conclusion, attribute sampling is a valuable tool in quality management and assurance. By focusing on specific attributes and utilizing a systematic approach to sampling, organizations can effectively monitor and maintain the quality of their products or services. Understanding the principles and applications of attribute sampling is crucial for professionals in various fields, as it enables them to make data-driven decisions that enhance overall quality and customer satisfaction.

在质量控制和统计分析领域，属性抽样的概念发挥着关键作用。这是一种用于根据某些属性确定一批物品是否符合特定质量标准的方法。与测量特征程度的变量抽样不同，属性抽样专注于通过/不通过标准或特定属性的存在/缺失。这使得它在质量特征本质上是二元的情况下特别有用，例如有缺陷或无缺陷的物品。 属性抽样的过程始于定义将要抽取样本的总体。这可以从制造的商品到服务输出不等。一旦建立了总体，就必须确定样本大小。样本大小至关重要，因为它影响从抽样过程中得出的结论的可靠性。较大的样本大小通常会产生更可靠的结果，但它也需要更多的资源和时间。在选择样本大小后，属性抽样的下一步是从总体中随机选择项目。随机选择至关重要，因为它有助于消除偏见，确保每个项目都有同等机会被纳入样本。这种随机性增强了发现的有效性，使其更具普遍适用性。一旦收集到样本，就会对其进行测试，以检查在过程开始时定义的特定属性。例如，如果一家公司生产灯泡，则属性可能是每个灯泡是否正常工作。然后将每个灯泡分类为“可接受”或“有缺陷”。这种分类结果可以提供有关整个生产批次整体质量的宝贵见解。属性抽样的结果通常以比例或百分比的形式表示。例如，如果在100个抽样的灯泡中发现5个有缺陷，则缺陷率为5%。此信息可以用于就整个批次的质量做出明智的决策。如果缺陷率超过可接受的限制，可以采取纠正措施以改善生产过程。属性抽样的一个显著优点是其简单性。该方法直接且易于实施，使其适用于各个行业。此外，它提供快速结果，使组织能够迅速响应质量问题。然而，必须认识到属性抽样也有其局限性。因为它仅考虑物品是否符合标准，所以它无法提供关于质量程度的信息。例如，它无法区分一个刚好达到质量标准的灯泡和一个远远超过标准的灯泡。 总之，属性抽样是质量管理和保证中的一种有价值工具。通过专注于特定属性并利用系统化的抽样方法，组织可以有效监控和维护其产品或服务的质量。理解属性抽样的原则和应用对于各个领域的专业人士至关重要，因为这使他们能够做出基于数据的决策，从而提高整体质量和客户满意度。