nominal error

简明释义

公称误差

英英释义

例句

1.Statistical methods are often used to quantify the nominal error in experimental results, which translates to 名义误差 in practical applications.

统计方法通常用于量化实验结果中的名义误差，这在实际应用中转化为名义误差。

2.In our latest report, we documented a nominal error of just 0.5%, indicating a 名义误差 that is acceptable for our standards.

在我们最新的报告中，我们记录了仅为0.5%的名义误差，这表明该名义误差符合我们的标准。

3.When analyzing the data, we found that the nominal error was within the expected range, showing a 名义误差 that did not affect the results significantly.

在分析数据时，我们发现名义误差在预期范围内，显示出名义误差并未显著影响结果。

4.The system was calibrated to minimize the nominal error, which refers to the 名义误差 in measurements.

系统经过校准以最小化名义误差，这指的是测量中的名义误差。

5.The engineer explained that any nominal error during the testing phase could be corrected before final production, meaning the 名义误差 can be adjusted.

工程师解释说，在测试阶段的任何名义误差都可以在最终生产之前进行修正，这意味着可以调整名义误差。

作文

In the field of statistics and data analysis, accuracy is paramount. However, even the most meticulous researchers cannot escape the concept of error. One term that often arises in discussions about measurement and analysis is nominal error. This phrase refers to a type of error that is considered minimal or insignificant in the context of the overall findings. Understanding nominal error is essential for anyone involved in research, as it helps clarify the reliability of results and the implications of minor inaccuracies.To illustrate the importance of nominal error, consider a scenario where a scientist is measuring the temperature of a substance using a thermometer. If the thermometer has a small calibration issue, it might read 0.1 degrees Celsius higher than the actual temperature. While this discrepancy exists, it is often classified as a nominal error because it does not significantly impact the conclusions drawn from the experiment. Researchers must be aware of such errors, but they can usually proceed with their analysis without significant concern.Another example can be found in survey research. When conducting surveys, researchers aim to gather data from a representative sample of the population. However, due to various factors such as respondent misunderstanding or sampling bias, there may be slight inaccuracies in the responses. These inaccuracies are often categorized as nominal errors. For instance, if a survey question is misinterpreted by only a small fraction of respondents, the resulting data may still be valid, as the overall trends and patterns remain intact despite the presence of these minor discrepancies.The recognition of nominal error allows researchers to focus on the larger picture rather than getting bogged down by every minor mistake. It encourages a more pragmatic approach to data interpretation, where the significance of findings is weighed against the potential for small errors. In scientific research, where precision is critical, distinguishing between major errors and nominal errors can be the difference between drawing valid conclusions and being misled by insignificant details.Moreover, acknowledging nominal error is crucial when it comes to reporting findings. Researchers often include margins of error in their reports, which help contextualize the data presented. By doing so, they inform readers of the potential for nominal errors while reassuring them that the core findings remain robust. This transparency fosters trust and credibility in the research process, as stakeholders can better understand the limitations and strengths of the study.In conclusion, the concept of nominal error serves as a reminder that perfection in measurement and analysis is unattainable. Instead of allowing minor inaccuracies to overshadow the significance of their work, researchers should embrace the idea of nominal error as a natural part of the research process. By doing so, they can maintain a balanced perspective, ensuring that their conclusions are based on sound evidence while remaining mindful of the inherent limitations of their methods. Ultimately, understanding nominal error equips researchers with the knowledge to navigate the complexities of data analysis, leading to more informed decision-making and more reliable outcomes in their respective fields.

在统计学和数据分析领域，准确性至关重要。然而，即使是最细致的研究人员也无法避免错误的概念。在关于测量和分析的讨论中，一个经常出现的术语是名义误差。这个短语指的是在整体发现的背景下被认为是微不足道或不重要的一种错误。理解名义误差对任何参与研究的人来说都是至关重要的，因为它有助于澄清结果的可靠性及轻微不准确的含义。为了说明名义误差的重要性，考虑一个场景：一位科学家正在使用温度计测量某种物质的温度。如果温度计有小的校准问题，它可能比实际温度高出0.1摄氏度。虽然这种差异存在，但通常被归类为名义误差，因为它并不显著影响实验得出的结论。研究人员必须意识到此类错误，但通常可以在没有重大担忧的情况下继续进行分析。另一个例子可以在调查研究中找到。当进行调查时，研究人员旨在从代表性样本中收集数据。然而，由于各种因素，如受访者误解或抽样偏差，回答中可能会存在轻微的不准确性。这些不准确性通常被归类为名义误差。例如，如果只有少数受访者误解了调查问题，尽管结果数据可能仍然有效，因为尽管存在这些轻微的差异，整体趋势和模式仍然保持不变。承认名义误差使研究人员能够关注更大的图景，而不是被每一个小错误所困扰。它鼓励一种更务实的数据解释方法，在这种方法中，研究结果的重要性与小错误的潜在性相权衡。在科学研究中，精确性至关重要，区分重大错误和名义误差可能是得出有效结论和被微不足道的细节误导之间的区别。此外，承认名义误差在报告研究结果时至关重要。研究人员通常在报告中包括误差范围，这有助于为所呈现的数据提供上下文。通过这样做，他们告知读者潜在的名义误差，同时向他们保证核心发现依然稳健。这种透明度在研究过程中培养了信任和信誉，因为利益相关者可以更好地理解研究的局限性和优势。总之，名义误差的概念提醒我们，测量和分析中的完美是不可达到的。研究人员应该接受轻微的不准确性不会掩盖他们工作的意义，而是将名义误差视为研究过程的自然部分。通过这样做，他们可以保持平衡的视角，确保他们的结论基于可靠的证据，同时对其方法固有的局限性保持警惕。最终，理解名义误差使研究人员具备应对数据分析复杂性的知识，从而在各自领域做出更明智的决策，获得更可靠的结果。