1.1 本指南简要介绍了一些公认的统计分析方法，这些方法有助于解释使用寿命数据。其目的是产生一个通用术语，并开发与使用寿命估计有关的通用方法和定量表达式。 1.2 本指南不涵盖详细的推导或特殊情况，而是涵盖了在使用寿命数据分析中应用的一系列方法。 1.3 本指南仅考虑在使用寿命数据分析中得到广泛接受的那些统计方法。 1.4 本指南强调了威布尔寿命分布模型，并详细介绍了使用寿命数据分析中常见情况的示例计算。 1.5 特定寿命分布模型的选择和使用应主要基于其与数据的拟合程度，以及在超出数据范围进行外推时是否会导致合理的预测。选择模型的进一步理由应基于理论考虑。 1.6 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《关于制定国际标准、指南和建议的原则的决定》中确立的国际公认标准化原则制定的。 ====意义和用途====== 4.1 使用寿命测试数据通常显示出与许多其他类型数据不同的分布形状。这是由于测量误差（通常为正态分布）的影响，再加上将使用寿命数据向早期故障（婴儿死亡率故障）或晚期故障时间（老化或磨损故障）倾斜的独特影响。本指南中的原则应用有助于研究者解释此类数据。 注2: 使用寿命或可靠性数据分析包在标准或通用计算机软件包中变得越来越容易获得。 这使数据简化和分析更容易掌握在越来越多的研究人员手中。

1.1 This guide presents briefly some generally accepted methods of statistical analyses which are useful in the interpretation of service life data. It is intended to produce a common terminology as well as developing a common methodology and quantitative expressions relating to service life estimation. 1.2 This guide does not cover detailed derivations, or special cases, but rather covers a range of approaches which have found application in service life data analyses. 1.3 Only those statistical methods that have found wide acceptance in service life data analyses have been considered in this guide. 1.4 The Weibull life distribution model is emphasized in this guide and example calculations of situations commonly encountered in analysis of service life data are covered in detail. 1.5 The choice and use of a particular life distribution model should be based primarily on how well it fits the data and whether it leads to reasonable projections when extrapolating beyond the range of data. Further justification for selecting a model should be based on theoretical considerations. 1.6 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 Service life test data often show different distribution shapes than many other types of data. This is due to the effects of measurement error (typically normally distributed), combined with those unique effects which skew service life data towards early failure (infant mortality failures) or late failure times (aging or wear-out failures) Applications of the principles in this guide can be helpful in allowing investigators to interpret such data. Note 2: Service life or reliability data analysis packages are becoming more readily available in standard or common computer software packages. This puts data reduction and analyses more readily into the hands of a growing number of investigators.