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.

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.