systematic error

简明释义

系统误差

英英释义

例句

1.The experiment showed a consistent systematic error in the measurements, indicating a flaw in the calibration of the equipment.

实验显示在测量中存在一致的系统误差，这表明设备的校准存在缺陷。

2.To minimize systematic error, we standardized our procedures across all trials.

为了最小化系统误差，我们在所有试验中标准化了我们的程序。

3.Researchers need to account for systematic error when analyzing data to ensure their results are valid.

研究人员在分析数据时需要考虑系统误差，以确保他们的结果有效。

4.In this study, we identified a systematic error that affected all our test subjects uniformly.

在这项研究中，我们识别出一个影响所有测试对象的系统误差。

5.The systematic error in the survey results led to incorrect conclusions about public opinion.

调查结果中的系统误差导致对公众意见的错误结论。

作文

In the realm of scientific research and experimentation, accuracy and precision are paramount. When conducting experiments, researchers strive to obtain results that are as close to the true value as possible. However, various factors can influence the outcomes of these experiments, leading to discrepancies between the observed values and the actual values. One such factor is what is known as a systematic error, which refers to a consistent, repeatable error associated with faulty equipment or a flawed experimental design. Understanding systematic error is crucial for scientists and researchers as it can significantly impact the validity of their findings.A systematic error can arise from several sources. For instance, if a measuring instrument is improperly calibrated, it may consistently give readings that are too high or too low. This type of error does not occur randomly; instead, it skews all measurements in one direction, leading to biased results. For example, if a scale is miscalibrated to always weigh 5 grams heavier than the actual weight, every measurement taken with that scale will reflect this bias. Consequently, researchers must be vigilant in ensuring that their instruments are correctly calibrated to minimize systematic error.Another common source of systematic error is the experimental design itself. If a researcher fails to account for certain variables that may affect the outcome of an experiment, the results may be misleading. For instance, in a clinical trial testing a new medication, if the participants are not randomly assigned to treatment and control groups, any observed effects may be due to pre-existing differences between the groups rather than the medication itself. This oversight can lead to a systematic error in interpreting the effectiveness of the treatment.Moreover, systematic error can also stem from human factors. Biases in data collection or analysis can lead to consistent errors in the results. For example, if a researcher has a preconceived notion about the outcome of an experiment, they may unconsciously interpret the data in a way that supports their hypothesis, thereby introducing systematic error. It is essential for researchers to remain objective and use blind or double-blind study designs to mitigate this risk.To address systematic error, researchers can implement several strategies. First and foremost, regular calibration and maintenance of equipment can help ensure that measurements are accurate. Additionally, employing rigorous experimental designs that account for potential confounding variables is vital. Researchers should also conduct pilot studies to identify any unforeseen biases or errors before proceeding with larger-scale experiments.Finally, statistical methods can be employed to detect and correct for systematic error. By analyzing data carefully, researchers can identify patterns that suggest the presence of systematic bias and take appropriate measures to adjust their findings accordingly.In conclusion, understanding and addressing systematic error is essential for the integrity of scientific research. By recognizing the sources of these errors and implementing strategies to minimize their impact, researchers can enhance the reliability of their findings and contribute to the advancement of knowledge in their respective fields. Ultimately, acknowledging the potential for systematic error not only improves individual studies but also strengthens the scientific community as a whole.

在科学研究和实验的领域中，准确性和精确性至关重要。当进行实验时，研究人员努力获得尽可能接近真实值的结果。然而，各种因素可能会影响这些实验的结果，导致观察值与实际值之间存在差异。其中一个因素就是被称为系统误差的东西，它指的是与故障设备或缺陷实验设计相关的持续、可重复的错误。理解系统误差对科学家和研究人员至关重要，因为它可能会显著影响他们发现的有效性。系统误差可能来自几个来源。例如，如果测量仪器校准不当，它可能会始终给出过高或过低的读数。这种类型的错误不会随机发生；相反，它会将所有测量偏向一个方向，从而导致结果偏倚。例如，如果一个秤校准错误，总是比实际重量重5克，那么用这个秤进行的每次测量都会反映这种偏差。因此，研究人员必须保持警惕，确保他们的仪器正确校准，以最小化系统误差。另一个常见的系统误差来源是实验设计本身。如果研究人员未能考虑可能影响实验结果的某些变量，结果可能会产生误导性。例如，在测试新药的临床试验中，如果参与者没有随机分配到治疗组和对照组，则观察到的任何效果可能是由于组之间的先前差异，而不是药物本身。这种疏忽可能导致在解释治疗有效性时出现系统误差。此外，系统误差也可能源于人类因素。数据收集或分析中的偏见可能导致结果中的一致性错误。例如，如果研究人员对实验结果有先入为主的看法，他们可能会无意中以支持自己假设的方式解读数据，从而引入系统误差。研究人员保持客观并使用盲法或双盲研究设计来减轻这一风险至关重要。为了应对系统误差，研究人员可以实施几个策略。首先，定期校准和维护设备可以帮助确保测量的准确性。此外，采用严格的实验设计，考虑潜在的混杂变量也是至关重要的。研究人员还应进行初步研究，以识别任何未预见的偏见或错误，然后再进行大规模实验。最后，可以采用统计方法来检测和纠正系统误差。通过仔细分析数据，研究人员可以识别出表明存在系统偏差的模式，并采取适当措施相应调整他们的发现。总之，理解和解决系统误差对于科学研究的完整性至关重要。通过识别这些错误的来源并实施策略以最小化其影响，研究人员可以提高其发现的可靠性，并为各自领域的知识进步做出贡献。最终，承认系统误差的潜在性不仅改善了个别研究，还增强了整个科学界。