error amount

简明释义

误差量

英英释义

例句

1.After reviewing the calculations, we found an error amount of 3% in the final results.

审核计算后，我们发现最终结果中的错误金额为3%。

2.We need to analyze the error amount in the data set to improve our model's accuracy.

我们需要分析数据集中错误金额以提高模型的准确性。

3.To ensure quality control, we must keep the error amount below 1%.

为了确保质量控制，我们必须将错误金额保持在1%以下。

4.The final report indicated an error amount of $500 in the budget calculations.

最终报告显示预算计算中存在错误金额为500美元。

5.The error amount in the measurements was within acceptable limits.

测量中的错误金额在可接受范围内。

作文

In the world of data analysis and scientific research, precision is paramount. Every experiment or study aims to produce results that are as accurate as possible. However, it is important to understand that no measurement is perfect, and there will always be some degree of uncertainty involved. This uncertainty can often be quantified in terms of the error amount (误差量). The error amount refers to the difference between the measured value and the true value. It is a critical concept that helps researchers assess the reliability of their results.When conducting experiments, scientists often use various tools and methods to gather data. Each of these tools has its own limitations and potential sources of error. For instance, when measuring the temperature of a liquid, a thermometer may have a specific range of accuracy. If the actual temperature is 100 degrees Celsius, but the thermometer reads 99.5 degrees, the error amount would be 0.5 degrees. Understanding this error amount is crucial for interpreting the results and determining whether they can be considered valid.Moreover, the error amount can also be classified into two types: systematic errors and random errors. Systematic errors are consistent and repeatable inaccuracies that occur due to flaws in the measurement system. For example, if a scale is improperly calibrated, it will consistently give readings that are higher or lower than the true weight. On the other hand, random errors are unpredictable fluctuations that can arise from various factors, such as environmental conditions or human error. Both types of errors contribute to the overall error amount and must be accounted for in any scientific analysis.In addition to scientific research, the concept of error amount is also applicable in fields such as finance, engineering, and quality control. In finance, for instance, analysts may calculate the error amount when forecasting future sales or profits. By understanding the potential error amount, businesses can make more informed decisions and develop strategies to mitigate risks.Furthermore, in engineering, the error amount plays a vital role in ensuring the safety and functionality of structures and systems. Engineers must consider the error amount when designing components to ensure they can withstand various stresses and strains. Failure to account for these errors can lead to catastrophic failures, highlighting the importance of precise measurements and calculations.In conclusion, the error amount is a fundamental concept that permeates various fields of study and practice. It serves as a reminder that while we strive for accuracy, we must also recognize the inherent uncertainties in our measurements. By understanding and quantifying the error amount, researchers, analysts, and engineers can enhance the reliability of their work and make more informed decisions. Ultimately, acknowledging the error amount allows us to approach problems with a more nuanced perspective, leading to better outcomes in both research and practical applications.

在数据分析和科学研究的世界里，精确性至关重要。每个实验或研究都旨在产生尽可能准确的结果。然而，重要的是要理解，没有任何测量是完美的，总会涉及某种程度的不确定性。这种不确定性通常可以通过误差量来量化。误差量是指测量值与真实值之间的差异。这是一个关键概念，帮助研究人员评估其结果的可靠性。在进行实验时，科学家们通常使用各种工具和方法来收集数据。这些工具各自都有其局限性和潜在的误差来源。例如，当测量液体的温度时，温度计可能具有特定的准确范围。如果实际温度为100摄氏度，但温度计读数为99.5度，则误差量为0.5度。理解这个误差量对于解释结果和确定其是否可以被视为有效至关重要。此外，误差量还可以分为两种类型：系统误差和随机误差。系统误差是由于测量系统中的缺陷而导致的一致且可重复的不准确性。例如，如果一个秤没有正确校准，它将始终给出高于或低于真实重量的读数。另一方面，随机误差是由于各种因素（如环境条件或人为错误）引起的不可预测的波动。这两种类型的误差都会对整体误差量产生影响，必须在任何科学分析中加以考虑。除了科学研究，误差量的概念在金融、工程和质量控制等领域也同样适用。例如，在金融领域，分析师在预测未来销售或利润时可能会计算误差量。通过理解潜在的误差量，企业可以做出更明智的决策，并制定减轻风险的策略。此外，在工程学中，误差量在确保结构和系统的安全性和功能性方面发挥着至关重要的作用。工程师在设计组件时必须考虑误差量，以确保它们能够承受各种应力和应变。如果未能考虑这些误差，可能会导致灾难性的故障，这突显了精确测量和计算的重要性。总之，误差量是一个基本概念，渗透到各种研究和实践领域。它提醒我们，虽然我们努力追求准确性，但我们也必须认识到测量中固有的不确定性。通过理解和量化误差量，研究人员、分析师和工程师可以提高其工作的可靠性，并做出更明智的决策。最终，承认误差量使我们能够以更细致的视角接近问题，从而在研究和实际应用中获得更好的结果。