Adequate measurement uncertainty evaluation is crucial for supporting basic measurement purposes. However, the most prevalent approach, the uncertainty propagation, may not be validly applicable under certain conditions which require the use of less restricted alternative method (MCM-based propagation of distributions). We demonstrate the effects of major conditions, e.g. non-linear measurement model, non-Gaussian input quantities, on the reliability of uncertainty evaluation methods, highlighting the importance of the less frequently examined impact of uncertainty component contributions to the standard uncertainty of measurand.

中文翻译：

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不确定性贡献对用于测量不确定性评估的方法的影响

足够的测量不确定度评估对于支持基本测量目的至关重要。但是，最普遍的方法（不确定性传播）在某些条件下可能无法有效地应用，这些条件要求使用限制较少的替代方法（基于MCM的分布传播）。我们证明了主要条件（例如非线性测量模型，非高斯输入量）对不确定性评估方法的可靠性的影响，强调了不经常检查的不确定性成分对测量对象标准不确定性影响的重要性。