1.1本实施规程作为规划实验室间试验的指南，以准备计算试验次数，以确定实施规程D 2905中讨论的纺织材料的平均质量，并根据实施规程D 2906中的要求制定精度声明。 1.2实验室间试验的规划需要对统计原理有一般的了解，包括使用方差分析估计的方差分量。应在咨询在实验设计和分析方面有经验且对测试方法中可能遇到的变异性的性质有一定了解的统计学家后，规划、执行和分析实验室间测试。 1.3本规程中的说明特别适用于以下各项的设计和分析: 1.3.1单实验室初步试验， 1.3.2中试规模实验室间试验，以及 1.3.3全尺寸实验室间试验。 1.4提供了有关分析前数据转换、缺失数据处理和外围观察处理的决策指南。 1.5本规程中给出的程序适用于基于正态分布或可通过变换变为正态分布的连续变量测量的试验方法。 获得合格的统计帮助( 1. )确定数据是否来自另一个已知分布，例如泊松分布( 2. )判断是否正常( 3. )将数据转换为更接近正态分布，或( 4. )使用规程D 4467。对于产生以下数据的试验方法，请使用实施规程D 4467中的程序:( 1. )非正态分布的连续数据或( 2. )离散数据，例如任意比例的评分，可以使用泊松分布建模的计数，或者可以使用二项式分布建模的指定数量试验中的成功比例或计数。 注1:关于实验室间测试和数据统计处理的其他信息，请参见实施规程D 1749、D 3040、E 173、E 177、E 691和术语E 456。 ====意义和用途====== 实验室间测试是一种确保对不同实验室、操作员、设备获得的结果的可变性进行估计的方法，当遵循特定试验方法中规定的程序，并确定该方法在多个实验室测试相同材料时，产生基本均匀可变性和一致水平的结果时。 根据实施规程D 2905和D 2906的指示，实验室间试验方差分量的估计提供了制备样本数量和精度声明所需的信息 .

1.1 This practice serves as a guide for planning interlaboratory tests in preparation for the calculation of the number of tests to determine the average quality of a textile material as discussed in Practice D 2905 and for the development of statements on precision as required in Practice D 2906. 1.2 The planning of interlaboratory tests requires a general knowledge of statistical principles including the use of variance components estimated from an analysis of variance. Interlaboratory tests should be planned, conducted, and analyzed after consultation with statisticians who are experienced in the design and analysis of experiments and who have some knowledge of the nature of the variability likely to be encountered in the test method. 1.3 The instructions in this practice are specifically applicable to design and analysis of: 1.3.1 Single laboratory preliminary trial, 1.3.2 Pilot-scale interlaboratory tests, and 1.3.3 Full-scale interlaboratory tests. 1.4 Guides for decisions pertaining to data transformations prior to analysis, the handling of missing data, and handling of outlying observations are provided. 1.5 Procedures given in this practice are applicable to test methods based on the measurement of continuous variates from normal distributions or from distributions which can be made normal by a transformation. Get qualified statistical help to ( 1 ) decide if the data are from another known distribution, such as the Poisson distribution, ( 2 ) make a judgment on normality, ( 3 ) transform data to a more nearly normal distribution, or ( 4 ) use Practice D 4467. Use the procedures in Practice D 4467 for test methods that produce data that are ( 1 ) continuous data that are not normally distributed or ( 2 ) discrete data, such as ratings on an arbitrary scale, counts that may be modelled by use of the Poisson distribution, or proportions or counts of successes in a specified number of trials that may be modelled by the binomial distribution. Note 1—Additional information on interlaboratory testing and on statistical treatment of data can be found in Practice D 1749, D 3040, E 173, E 177, E 691, and Terminology E 456. ====== Significance And Use ====== Interlaboratory testing is a means of securing estimates of the variability in results obtained by different laboratories, operators, equipment, and environments when following procedures prescribed in a specific test method and of determining that the method produces results of essentially uniform variability and at a consistent level when the same materials are tested in a number of laboratories. The estimates of the components of variance from the interlaboratory test provide the information needed for the preparation of statements on the number of specimens and on precision as directed in Practices D 2905 and D 2906 .