admissible error

简明释义

容许误差

英英释义

例句

1.For this type of analysis, the admissible error is defined as 0.1 units.

对于这种类型的分析，可接受误差定义为0.1个单位。

2.The results are valid within the admissible error range specified by the manufacturer.

结果在制造商指定的可接受误差范围内有效。

3.The measurement of the temperature has an admissible error of ±0.5 degrees Celsius.

温度测量的可接受误差为±0.5摄氏度。

4.When calibrating the instrument, it is important to note the admissible error allowed.

在校准仪器时，注意允许的可接受误差是很重要的。

5.In this experiment, we consider an admissible error of 2% for our calculations.

在这个实验中，我们考虑计算中的可接受误差为2%。

作文

In the realm of statistics and data analysis, the concept of admissible error plays a crucial role in understanding the limitations of our measurements and predictions. An admissible error refers to the range of acceptable inaccuracies that can be tolerated in a given measurement or estimation without compromising the overall validity of the results. This idea is particularly significant in fields such as engineering, economics, and scientific research, where precision is paramount yet often difficult to achieve. For instance, consider a scenario in which a civil engineer is tasked with designing a bridge. The engineer must take into account various factors, including the weight the bridge must support, environmental conditions, and material properties. In this context, the admissible error might define the maximum allowable deviation in the load calculations. If the calculations are within this range, the design can be deemed safe and effective. However, exceeding this admissible error could lead to catastrophic failures, highlighting the importance of understanding and adhering to these limits.Moreover, in the field of economics, policymakers often rely on statistical models to forecast economic trends. These models, however, are based on numerous assumptions and simplifications, which can introduce errors. By recognizing the admissible error in their forecasts, policymakers can make more informed decisions. For example, if a model predicts a 3% growth rate with an admissible error of ±0.5%, the actual growth rate could reasonably fall anywhere between 2.5% and 3.5%. Understanding this range allows for better strategic planning and resource allocation.In scientific research, the concept of admissible error becomes even more critical. Researchers must often deal with variability in their data due to factors such as measurement techniques, sample sizes, and environmental influences. By defining an admissible error, researchers can determine whether their findings are statistically significant or if they fall within the expected range of variation. For example, when conducting an experiment to test a new drug, researchers might establish an admissible error in the efficacy rates. If the observed results fall within this range, the drug may be considered effective; otherwise, further investigation would be necessary.Understanding admissible error also has implications for quality control in manufacturing processes. Companies often set standards for product specifications, and any deviations from these specifications can lead to defects. By establishing an admissible error threshold, manufacturers can ensure that products meet quality standards while allowing for minor variations that do not significantly impact functionality. This balance between precision and practicality is essential for maintaining customer satisfaction and operational efficiency.In conclusion, the concept of admissible error is vital across various fields, serving as a guideline for acceptable inaccuracies in measurements and predictions. Whether in engineering, economics, scientific research, or manufacturing, understanding and applying the principles of admissible error enables professionals to make informed decisions, mitigate risks, and enhance the reliability of their results. As we continue to advance in technology and data analysis, the significance of recognizing and managing admissible error will only grow, underscoring its importance in achieving accuracy and credibility in our work.

在统计学和数据分析领域，可接受误差的概念在理解我们测量和预测的局限性方面起着至关重要的作用。可接受误差是指在给定测量或估计中可以容忍的可接受不准确性范围，而不会影响结果的整体有效性。这个理念在工程、经济学和科学研究等领域尤为重要，在这些领域，精确度至关重要，但往往难以实现。例如，考虑一个土木工程师被委托设计一座桥梁的场景。工程师必须考虑多个因素，包括桥梁必须承受的重量、环境条件和材料特性。在这种情况下，可接受误差可能定义了负载计算中允许的最大偏差。如果计算在这个范围内，设计可以被认为是安全和有效的。然而，超过这个可接受误差可能导致灾难性的失败，这突显了理解和遵守这些限制的重要性。此外，在经济学领域，政策制定者通常依赖统计模型来预测经济趋势。然而，这些模型是基于许多假设和简化，这可能引入误差。通过认识到他们预测中的可接受误差，政策制定者可以做出更明智的决策。例如，如果一个模型预测增长率为3%，而可接受误差为±0.5%，那么实际增长率合理地可以在2.5%到3.5%之间。理解这个范围使得更好的战略规划和资源分配成为可能。在科学研究中，可接受误差的概念变得更加关键。研究人员必须处理数据中的变异性，这些变异性可能由于测量技术、样本大小和环境影响等因素而产生。通过定义可接受误差，研究人员可以确定他们的发现是否具有统计显著性，或者是否在预期的变化范围内。例如，在进行新药测试的实验时，研究人员可能会建立可接受误差在疗效率上的标准。如果观察到的结果落在这个范围内，该药物可能被认为是有效的；否则，则需要进一步调查。理解可接受误差还对制造过程中的质量控制具有重要意义。公司通常为产品规格设定标准，任何偏离这些规格的情况都可能导致缺陷。通过建立可接受误差阈值，制造商可以确保产品符合质量标准，同时允许不会显著影响功能的轻微变化。这种精确与实用之间的平衡对于维护客户满意度和运营效率至关重要。总之，可接受误差的概念在各个领域都是至关重要的，作为测量和预测中可接受不准确性的指导方针。无论是在工程、经济学、科学研究还是制造业，理解和应用可接受误差的原则使专业人士能够做出明智的决策、降低风险并增强其结果的可靠性。随着我们在技术和数据分析方面的不断进步，认识和管理可接受误差的重要性只会增加，突显了其在实现工作准确性和可信度方面的重要性。