1.1 本规程介绍了在使用属性法且样本中无响应的情况下，关于未知不合格分数或数量或不合格发生率的置信上限设定方法。考虑了三种情况。 1.1.1 样本是从一个过程或大量离散项目中选择的，样本中不合格项目的数量为零。 1.1.2 从有限批离散项目中随机选择项目样本，样本中不合格项目的数量为零。 1.1.3 样本是连续体（时间、空间、体积、面积等）的一部分。 )样品中的不符合项数量为零。 1.2 在这种实践中，允许误分类误差，但仅当误分类率被充分理解或已知并且可以用数字近似时。 1.3 以英寸-磅单位表示的值应视为标准值。本标准不包括其他测量单位。 1.4 本标准并不旨在解决与其使用相关的所有安全问题（如有）。本标准的使用者有责任在使用前建立适当的安全、健康和环境实践，并确定监管限制的适用性。 1.5 本国际标准是根据世界贸易组织技术性贸易壁垒（TBT）委员会发布的《国际标准、指南和建议制定原则决定》中确立的国际公认标准化原则制定的。 =====意义和用途====== 4.1 在情况1中，样品选自过程或非常大的感兴趣群体。总体基本上是无限的，每个项都有或没有定义的属性。人口（进程）中有一部分未知的项目 p （长期平均过程不合格）具有该属性。样本是一组 n 从所考虑的过程或总体中随机选择的离散项目，并且该属性不在样本中显示。目的是确定置信上限， p u ，对于未知分数 p 由此可以声称 p ≤ p u 具有一定的置信系数（概率） C 在这种情况下，二项分布是抽样分布。 4.2 在案例2中 n 项目是从有限数量的 N 项目。与情况1类似，每个项目要么具有或不具有定义的属性， D ，属于具有属性的项。示例不显示该属性。目的是确定置信上限， D u ，对于未知号码 D ，由此可以声称 D ≤ D u 具有一定的置信系数（概率） C 在这种情况下，超几何分布是采样分布。 4.3 在案例3中，有一个过程，但输出是一个连续体，例如面积（例如，一卷纸或其他材料、一块作物）、体积（例如，液体或气体的体积）或时间（例如，小时、天、季度等）。样本大小定义为采样的“连续体”部分，定义的属性可能在采样部分出现任何次数。 在感兴趣的连续体的采样间隔上，定义的属性存在未知的平均发生率λ。示例不显示该属性。对于一卷纸，每100英尺可能有瑕疵 2. ; 对于一定体积的液体，每立方升微生物数；每英亩作物的孢子数；在一段时间间隔内，每小时的呼叫数、每天的客户数或每季度的事故数。利率λ与感兴趣区间的大小成比例。因此，如果λ=每100英尺12个瑕疵 2. 这相当于每10英尺有1.2个瑕疵 2. 或每250个瑕疵30个 英尺 2. 。在分析和解释中，务必记住间隔的大小。目标是确定置信上限λ u ，对于未知的发生率λ，可以声称λ ≤ λ u 具有一定的置信系数（概率） C 泊松分布是这种情况下的抽样分布。 4.4 情况3的一个变化是，采样的“间隔”实际上是一组离散项，并且定义的属性可以在一个项中出现任意次数。在这种情况下，连续体是一个生产离散项目（如金属零件）的过程，属性被定义为划痕。 任何单个项目上都可能出现任何数量的划痕。在这种情况下，出现率λ可以定义为每1000个零件的划痕或类似的度量。 4.5 在每种情况下，检查项目样本或连续体的一部分是否存在已定义的属性，并且不观察该属性（即零响应）。目的是确定未知比例的置信上限， p （情况1）， D （情况2）或未知发生率λ（情况3）。在这种实践中，置信度意味着未知参数不超过上限的概率。 更一般地，这些方法确定样本量、置信度和置信上限之间的关系。它们可用于确定证明特定 p , D ，或具有一定置信度的λ。它们还可用于确定在证明特定的 p , D ，或λ。 4.6 在这种实践中，允许误分类误差，但仅当误分类率被充分理解或已知并且可以用数字近似时。 4.7 可以将经典接受抽样理论的语言应用于该方法。 本规程中不使用批次公差百分比、可接受质量水平和消费者质量水平等术语。有关这些术语的更多信息，请参阅实践 1994年 .

1.1 This practice presents methodology for the setting of an upper confidence bound regarding a unknown fraction or quantity non-conforming, or a rate of occurrence for nonconformities, in cases where the method of attributes is used and there is a zero response in a sample. Three cases are considered. 1.1.1 The sample is selected from a process or a very large population of discrete items, and the number of non-conforming items in the sample is zero. 1.1.2 A sample of items is selected at random from a finite lot of discrete items, and the number of non-conforming items in the sample is zero. 1.1.3 The sample is a portion of a continuum (time, space, volume, area, etc.) and the number of non-conformities in the sample is zero. 1.2 Allowance is made for misclassification error in this practice, but only when misclassification rates are well understood or known and can be approximated numerically. 1.3 The values stated in inch-pound units are to be regarded as standard. No other units of measurement are included in this standard. 1.4 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety, health, and environmental practices and determine the applicability of regulatory limitations prior to use. 1.5 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee. ====== Significance And Use ====== 4.1 In Case 1, the sample is selected from a process or a very large population of interest. The population is essentially unlimited, and each item either has or has not the defined attribute. The population (process) has an unknown fraction of items p (long run average process non-conforming) having the attribute. The sample is a group of n discrete items selected at random from the process or population under consideration, and the attribute is not exhibited in the sample. The objective is to determine an upper confidence bound, p u , for the unknown fraction p whereby one can claim that p ≤ p u with some confidence coefficient (probability) C . The binomial distribution is the sampling distribution in this case. 4.2 In Case 2, a sample of n items is selected at random from a finite lot of N items. Like Case 1, each item either has or has not the defined attribute, and the population has an unknown number, D , of items having the attribute. The sample does not exhibit the attribute. The objective is to determine an upper confidence bound, D u , for the unknown number D , whereby one can claim that D ≤ D u with some confidence coefficient (probability) C . The hypergeometric distribution is the sampling distribution in this case. 4.3 In Case 3, there is a process, but the output is a continuum, such as area (for example, a roll of paper or other material, a field of crop), volume (for example, a volume of liquid or gas), or time (for example, hours, days, quarterly, etc.) The sample size is defined as that portion of the “continuum” sampled, and the defined attribute may occur any number of times over the sampled portion. There is an unknown average rate of occurrence, λ, for the defined attribute over the sampled interval of the continuum that is of interest. The sample does not exhibit the attribute. For a roll of paper, this might be blemishes per 100 ft 2 ; for a volume of liquid, microbes per cubic litre; for a field of crop, spores per acre; for a time interval, calls per hour, customers per day or accidents per quarter. The rate, λ, is proportional to the size of the interval of interest. Thus, if λ = 12 blemishes per 100 ft 2 of paper, this is equivalent to 1.2 blemishes per 10 ft 2 or 30 blemishes per 250 ft 2 . It is important to keep in mind the size of the interval in the analysis and interpretation. The objective is to determine an upper confidence bound, λ u , for the unknown occurrence rate λ, whereby one can claim that λ ≤ λ u with some confidence coefficient (probability) C . The Poisson distribution is the sampling distribution in this case. 4.4 A variation on Case 3 is the situation where the sampled “interval” is really a group of discrete items, and the defined attribute may occur any number of times within an item. This might be the case where the continuum is a process producing discrete items such as metal parts, and the attribute is defined as a scratch. Any number of scratches could occur on any single item. In such a case, the occurrence rate, λ, might be defined as scratches per 1000 parts or some similar metric. 4.5 In each case, a sample of items or a portion of a continuum is examined for the presence of a defined attribute, and the attribute is not observed (that is, a zero response). The objective is to determine an upper confidence bound for either an unknown proportion, p (Case 1), an unknown quantity, D (Case 2), or an unknown rate of occurrence, λ (Case 3). In this practice, confidence means the probability that the unknown parameter is not more than the upper bound. More generally, these methods determine a relationship among sample size, confidence and the upper confidence bound. They can be used to determine the sample size required to demonstrate a specific p , D , or λ with some degree of confidence. They can also be used to determine the degree of confidence achieved in demonstrating a specified p , D , or λ. 4.6 In this practice, allowance is made for misclassification error but only when misclassification rates are well understood or known, and can be approximated numerically. 4.7 It is possible to impose the language of classical acceptance sampling theory on this method. Terms such as lot tolerance percent defective, acceptable quality level, and consumer quality level are not used in this practice. For more information on these terms, see Practice E1994 .