systematic error

简明释义

系统错误

英英释义

例句

1.We need to account for systematic error 系统误差 when calibrating our instruments.

在校准我们的仪器时，我们需要考虑systematic error 系统误差。

2.The systematic error 系统误差 in the data analysis led to incorrect conclusions.

数据分析中的systematic error 系统误差导致了错误的结论。

3.In our latest experiment, we discovered a systematic error 系统误差 in the measurement of temperature.

在我们最新的实验中，我们发现了温度测量中的一个systematic error 系统误差。

4.The systematic error 系统误差 caused by faulty equipment was significant enough to affect the results.

由于设备故障造成的systematic error 系统误差足以影响结果。

5.To improve accuracy, we must identify and correct any systematic error 系统误差 in our measurements.

为了提高准确性，我们必须识别并纠正我们测量中的任何systematic error 系统误差。

作文

In the realm of scientific research and data analysis, the concept of systematic error plays a crucial role in determining the accuracy and reliability of results. A systematic error refers to a consistent, repeatable error associated with faulty equipment or a flawed experimental design. Unlike random errors, which can vary in magnitude and direction, systematic errors are predictable and often arise from biases in measurement processes. Understanding and addressing these errors is essential for scientists and researchers to ensure the integrity of their findings.For instance, consider a scenario where a thermometer consistently reads temperatures that are 2 degrees higher than the actual temperature due to calibration issues. This discrepancy would introduce a systematic error in any experiments relying on that thermometer for temperature measurements. As a result, all readings taken with this instrument would be affected, leading to potentially misleading conclusions. If researchers fail to recognize and correct for this systematic error, their work could contribute to incorrect theories or practices in their field.Moreover, systematic errors can also stem from human factors, such as biases in data collection or interpretation. For example, if a researcher has a preconceived notion about the outcome of an experiment, they may unconsciously favor data that supports their hypothesis while disregarding data that contradicts it. This type of bias introduces a systematic error into the research process, affecting the validity of the results.To mitigate systematic errors, researchers employ various strategies. One common approach is to calibrate instruments regularly to ensure they provide accurate measurements. In the case of our thermometer, recalibrating it against a known standard would help eliminate the systematic error. Additionally, researchers can use control groups and randomization techniques to minimize biases in experimental design, thus reducing the likelihood of systematic errors arising from human influence.Another important aspect of dealing with systematic errors is transparency in reporting research methods and results. By clearly documenting the procedures followed and any potential sources of bias, researchers allow others in the scientific community to critically evaluate their work. This openness fosters collaboration and encourages the replication of studies, which is vital for confirming findings and identifying any lingering systematic errors.Ultimately, recognizing and addressing systematic errors is fundamental to the pursuit of knowledge. Scientists must remain vigilant in their efforts to identify potential sources of bias and strive for objectivity in their research. By doing so, they not only enhance the credibility of their own work but also contribute to the collective understanding of their respective fields.In conclusion, the significance of systematic error in scientific research cannot be overstated. These errors can arise from faulty equipment, flawed experimental designs, or even human biases. To ensure the accuracy and reliability of research findings, it is imperative for researchers to identify, address, and report systematic errors transparently. By prioritizing the integrity of their work, scientists can advance knowledge and foster trust within the scientific community, ultimately benefiting society as a whole.

在科学研究和数据分析领域，系统误差的概念在确定结果的准确性和可靠性方面起着至关重要的作用。系统误差是指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，随机误差的大小和方向可能会变化，而系统误差是可预测的，通常源于测量过程中的偏差。理解和解决这些错误对于科学家和研究人员确保其发现的完整性至关重要。例如，考虑一个场景，其中由于校准问题，温度计始终比实际温度高出2度。这一差异会在依赖该温度计进行温度测量的任何实验中引入系统误差。因此，使用此仪器进行的所有读数都会受到影响，导致可能误导性的结论。如果研究人员未能识别和纠正这一系统误差，他们的工作可能会对其领域中的错误理论或实践做出贡献。此外，系统误差也可能源于人为因素，例如数据收集或解释中的偏见。例如，如果研究人员对实验结果有先入为主的看法，他们可能会无意中偏向支持其假设的数据，同时忽视与之相矛盾的数据。这种类型的偏见在研究过程中引入了系统误差，影响结果的有效性。为了减轻系统误差，研究人员采用各种策略。一种常见的方法是定期校准仪器，以确保其提供准确的测量。在我们温度计的例子中，将其与已知标准重新校准将有助于消除系统误差。此外，研究人员可以使用对照组和随机化技术，以最小化实验设计中的偏见，从而减少由人为影响引起的系统误差的可能性。处理系统误差的另一个重要方面是透明地报告研究方法和结果。通过清楚地记录所遵循的程序以及任何潜在的偏见来源，研究人员使科学界的其他人能够批判性地评估他们的工作。这种开放性促进了合作并鼓励研究的复制，这对于确认发现和识别任何残留的系统误差至关重要。最终，认识和解决系统误差是追求知识的基础。科学家必须保持警惕，努力识别潜在的偏见来源，并在研究中追求客观性。通过这样做，他们不仅增强了自己工作的可信度，还为各自领域的共同理解做出了贡献。总之，系统误差在科学研究中的重要性不容小觑。这些错误可能源于故障设备、缺陷的实验设计或甚至人为偏见。为了确保研究结果的准确性和可靠性，研究人员必须透明地识别、解决和报告系统误差。通过优先考虑工作的完整性，科学家可以推动知识进步，并在科学界内建立信任，最终惠及整个社会。