variable error

简明释义

不定误差

英英释义

例句

1.In statistical analysis, controlling for variable error is crucial to ensure accurate results.

在统计分析中，控制变量误差对于确保结果准确至关重要。

2.The technician noted a high level of variable error in the measurements, which indicates inconsistency in the data collection process.

技术人员注意到测量中存在较高的变量误差，这表明数据收集过程不一致。

3.The experiment was repeated multiple times to minimize variable error and achieve reliable outcomes.

实验重复多次以最小化变量误差，以获得可靠的结果。

4.Researchers must account for variable error when interpreting the findings of their studies.

研究人员在解读研究结果时必须考虑变量误差。

5.The variable error observed in the results can often be attributed to environmental factors.

结果中观察到的变量误差通常可以归因于环境因素。

作文

In the realm of statistics and data analysis, understanding the concept of variable error is crucial for interpreting results accurately. Variable error refers to the inconsistencies or fluctuations in measurements that occur when the same quantity is measured multiple times. This type of error can arise from various sources, including instrument precision, environmental conditions, and human factors. For instance, if a scientist measures the temperature of a solution several times using the same thermometer, the readings may differ slightly due to these influences. To illustrate the impact of variable error, consider a scenario in a laboratory where researchers are trying to determine the boiling point of water. They might conduct multiple trials and record different temperatures each time, such as 99.5°C, 100.1°C, and 99.8°C. These variations highlight the presence of variable error in their measurements. While the true boiling point of water at standard atmospheric pressure is 100°C, the discrepancies in their results demonstrate how variable error can complicate data interpretation.It is essential to distinguish variable error from systematic error, which is a consistent, repeatable error that affects all measurements in a predictable way. Unlike variable error, which can fluctuate randomly, systematic error tends to skew results in one direction. For example, if a scale is improperly calibrated, it might consistently weigh objects heavier than they are, leading to a systematic error in weight measurement.Addressing variable error is vital for enhancing the reliability of experimental results. Researchers often employ various strategies to minimize this type of error, such as increasing sample size, using more precise instruments, and conducting experiments under controlled conditions. By doing so, they can improve the accuracy of their findings and draw more reliable conclusions.Moreover, statistical techniques can be employed to analyze variable error. For instance, calculating the standard deviation of a set of measurements can provide insight into the degree of variability present. A low standard deviation indicates that the measurements are closely clustered around the mean, suggesting minimal variable error, while a high standard deviation indicates greater dispersion and, consequently, more variable error.In conclusion, recognizing and understanding variable error is fundamental in the fields of science and research. It serves as a reminder that measurements are not always perfect and that variability is an inherent aspect of data collection. By acknowledging the presence of variable error and implementing strategies to mitigate its effects, researchers can enhance the validity of their conclusions and contribute to the advancement of knowledge in their respective fields. Ultimately, the goal is to strive for precision and accuracy in measurements, ensuring that the insights drawn from data are as reliable as possible.

在统计学和数据分析领域，理解变量误差的概念对于准确解读结果至关重要。变量误差是指在多次测量同一数量时出现的不一致性或波动。这种类型的误差可能源于各种因素，包括仪器精度、环境条件和人为因素。例如，如果科学家使用同一温度计多次测量溶液的温度，读数可能会因这些影响而略有不同。为了说明变量误差的影响，考虑一个实验室的场景，研究人员试图确定水的沸点。他们可能进行多次试验，并记录不同的温度，例如99.5°C、100.1°C和99.8°C。这些变化突显了他们测量中存在的变量误差。虽然标准大气压下水的真实沸点为100°C，但结果中的差异表明变量误差如何使数据解读变得复杂。区分变量误差和系统误差是至关重要的，后者是一种一致的、可重复的误差，以可预测的方式影响所有测量。与可能随机波动的变量误差不同，系统误差往往会在一个方向上扭曲结果。例如，如果一个秤校准不当，它可能会始终将物体称重为比实际重，从而导致重量测量中的系统误差。解决变量误差对于提高实验结果的可靠性至关重要。研究人员通常采用各种策略来最小化这种类型的误差，例如增加样本量、使用更精确的仪器以及在受控条件下进行实验。通过这样做，他们可以提高研究结果的准确性并得出更可靠的结论。此外，可以采用统计技术来分析变量误差。例如，计算一组测量值的标准差可以提供关于存在的变异程度的见解。低标准差表明测量值紧密聚集在均值周围，暗示变量误差较小，而高标准差则表明分散程度较大，因此变量误差更多。总之，认识和理解变量误差在科学和研究领域至关重要。它提醒我们测量并非总是完美的，变异是数据收集的固有方面。通过承认变量误差的存在并实施策略来减轻其影响，研究人员可以增强其结论的有效性，并为各自领域知识的进步做出贡献。最终目标是努力追求测量的精确性和准确性，确保从数据中得出的见解尽可能可靠。