systemic error

简明释义

系统误差T

英英释义

例句

1.The software update fixed a systemic error that caused frequent crashes.

软件更新修复了一个导致频繁崩溃的系统性错误。

2.The research team discovered a systemic error in their data collection process that affected the overall results.

研究团队发现他们的数据收集过程中存在一个系统性错误，影响了总体结果。

3.Engineers must account for systemic error when designing systems to ensure reliability.

工程师在设计系统时必须考虑到系统性错误以确保可靠性。

4.The audit revealed a systemic error in the financial reporting that led to significant discrepancies.

审计揭示了财务报告中的一个系统性错误，导致了重大差异。

5.To improve accuracy, we need to identify and eliminate any systemic error in our calculations.

为了提高准确性，我们需要识别并消除我们计算中的任何系统性错误。

作文

In the field of science and engineering, accuracy is paramount. When conducting experiments or analyzing data, researchers strive to minimize errors that could lead to misleading conclusions. Among the various types of errors that can occur, one particularly significant category is known as systemic error. A systemic error (系统误差) refers to a consistent, repeatable error associated with faulty equipment or a flawed experimental design. Unlike random errors, which vary unpredictably, systemic errors are predictable and often arise from a specific source. Understanding these errors is crucial for improving the reliability of scientific measurements and ensuring valid results.One common example of a systemic error occurs in the calibration of measuring instruments. If a thermometer is consistently off by a few degrees due to improper calibration, every temperature reading taken with that thermometer will be affected. This consistent deviation leads to a systemic error that skews the results of any experiment relying on that measurement. Researchers must recognize and account for such errors to maintain the integrity of their findings.Another instance of systemic error can be found in survey methodologies. For example, if a survey is designed in a way that consistently leads respondents towards a particular answer, the results will reflect this bias. This is a systemic error because it originates from the survey design rather than random variations in responses. Researchers must carefully consider how questions are phrased and the order in which they are presented to avoid introducing bias into their findings.Additionally, systemic errors can occur in statistical analyses. For instance, if a dataset is collected from a non-representative sample, the conclusions drawn from that data may not accurately reflect the broader population. This type of systemic error can lead to misguided policies or ineffective interventions based on flawed assumptions about the population being studied. To mitigate such errors, researchers should employ rigorous sampling techniques and ensure that their samples are representative of the population as a whole.The implications of systemic errors extend beyond individual studies; they can affect entire fields of research. For example, if a particular measurement technique is widely adopted despite a known systemic error, subsequent studies that rely on that technique may propagate the error throughout the literature. This can create a false sense of confidence in findings that are, in reality, based on flawed data. Therefore, it is essential for scientists to continuously evaluate and refine their methods to minimize systemic errors.In conclusion, understanding and addressing systemic errors (系统误差) is vital for the advancement of scientific knowledge. By recognizing the sources of these errors and implementing strategies to mitigate them, researchers can enhance the accuracy and reliability of their work. Whether through careful calibration of instruments, thoughtful survey design, or rigorous sampling methods, minimizing systemic errors is a fundamental aspect of conducting high-quality research. As we strive for greater precision in our measurements and analyses, acknowledging the potential for systemic errors will ultimately lead to more trustworthy scientific outcomes.

在科学和工程领域，准确性至关重要。在进行实验或分析数据时，研究人员努力最小化可能导致误导性结论的错误。在各种可能发生的错误类型中，有一种特别重要的类别被称为系统误差。系统误差（系统误差）指的是与故障设备或实验设计缺陷相关的持续、可重复的错误。与随机误差不同，随机误差是不确定的而且变化无常，系统误差是可预测的，通常源于特定来源。理解这些错误对于提高科学测量的可靠性和确保结果的有效性至关重要。一个常见的系统误差例子出现在测量仪器的校准中。如果由于不当校准，温度计始终偏离几度，那么使用该温度计进行的每次温度读数都会受到影响。这种一致的偏差导致了一个系统误差，使依赖该测量的任何实验结果产生偏差。研究人员必须识别并考虑这种错误，以维护其发现的完整性。另一个系统误差的实例可以在调查方法中找到。例如，如果一个调查的设计方式始终引导受访者朝着特定答案回答，那么结果将反映这种偏见。这是一个系统误差，因为它源于调查设计，而不是响应中的随机变化。研究人员必须仔细考虑问题的措辞以及它们呈现的顺序，以避免在他们的发现中引入偏见。此外，系统误差还可能发生在统计分析中。例如，如果一个数据集是从非代表性的样本中收集的，那么从该数据得出的结论可能无法准确反映更广泛的人群。这种类型的系统误差可能导致基于对研究人群的错误假设的误导性政策或无效干预。为了减轻这种错误，研究人员应采用严格的抽样技术，并确保他们的样本能够代表整个研究人群。系统误差的影响超出了单个研究；它们可能影响整个研究领域。例如，如果某种特定的测量技术被广泛采用，尽管已知存在系统误差，那么依赖该技术的后续研究可能会在文献中传播该错误。这可能导致对实际上基于有缺陷数据的发现产生虚假的信心。因此，科学家必须不断评估和改进他们的方法，以最小化系统误差。总之，理解和解决系统误差（系统误差）对于科学知识的进步至关重要。通过识别这些错误的来源并实施减轻策略，研究人员可以提高其工作的准确性和可靠性。无论是通过仔细校准仪器、周到的调查设计，还是严格的抽样方法，最小化系统误差是进行高质量研究的基本方面。随着我们在测量和分析中追求更大的精确度，承认系统误差的潜在性将最终导致更可信的科学结果。