1.1 通过对单个样本上的多个测量结果进行统计分析，或通过测量多个样本（以适当的为准），可以减少与物体颜色样本的平均颜色或色差测量相关的可变性。 1.2 本规程提供了一种方法，用于确定将可变性降低到相关颜色或色差公差的预定分数所需的测量数量。 1.3 这种做法在范围上是通用的，而不是具体的仪器或材料。 ====意义和用途====== 5.1 无论何时测量颜色，都应使用此做法- 将比例或色差比例值与确定的公差进行比较。通过这种方式，可以定量证明采样和测量程序足以明确决定平均结果是否在公差范围内。 5.2 本规程基于SAE J的部分内容 1545，因为它适用于涂漆或塑料汽车零件。它通常适用于各种材料中的物体颜色。织物等纹理材料可能需要特别考虑（见SAE J 1545和STP 15D数据表示和控制图分析手册 5. ). 5.3 边练习边练习 E178 关于异常值，它不包括与盒须技术相关的定义。 离群值的定义是可操作的，有点模糊，因为对于什么是离群值仍然存在很大的分歧。在任何正态分布的人口中，都会有从负到正无穷大的成员。理论上，根据人口参数的估计，应该在任何样本中包括人口的任何成员。实际上，包括在小样本中发现的远离平均值的成员，其中大多数成员在平均值附近，将在总体参数（平均值、标准差、标准误差）的估计中引入系统偏差。这种偏差与本实践的目标直接相反，即减少测量可变性的影响。 就本实践而言，不区分抽样误差和分布尾部成员。实践 E178 有几种方法和显著性表，试图区分这两种类型的极值。

1.1 Reduction of the variability associated with average color or color-difference measurements of object-color specimens is achieved by statistical analysis of the results of multiple measurements on a single specimen, or by measurement of multiple specimens, whichever is appropriate. 1.2 This practice provides a means for the determination of the number of measurements required to reduce the variability to a predetermined fraction of the relevant color or color-difference tolerances. 1.3 This practice is general in scope rather than specific as to instrument or material. ====== Significance And Use ====== 5.1 This practice should be used whenever measured color-scale or color-difference-scale values are to be compared to an established tolerance. In this way it can be demonstrated quantitatively that the sampling and measurement procedures are adequate to allow an unambiguous decision as to whether or not the mean results are within tolerance. 5.2 This practice is based on portions of SAE J 1545, as it applies to painted or plastic automotive parts. It is generally applicable to object colors in various materials. Textured materials, such as textiles, may require special consideration (see SAE J 1545 and STP 15D Manual on Presentation of Data and Control Chart Analysis 5 ). 5.3 While Practice E178 deals with outliers, it does not include definitions relating to the box and whisker technique. The definition of an outlier is operational and a little vague because there is still considerable disagreement about what constitutes an outlier. In any normally distributed population, there will be members that range from minus to plus infinity. Theoretically, one should include any member of the population in any sample based on estimates of the population parameters. Practically, including a member that is found far from the mean within a small sample, most members of which are found near the mean, will introduce a systematic bias into the estimate of the population parameters (mean, standard deviation, standard error). Such a bias is in direct contrast with the goal of this practice, namely, to reduce the effects of variability of measurement. For the purposes of this practice, no distinction is made between errors of sampling and members of the tails of the distribution. Practice E178 has several methods and significance tables to attempt to differentiate between these two types of extreme values.