robust statistic

简明释义

稳健统计量

英英释义

例句

1.Using a robust statistic can improve the validity of your findings in the presence of noise.

在存在噪声的情况下，使用稳健统计量可以提高你发现的有效性。

2.The robust statistic helped us draw more reliable conclusions from the data.

这个稳健统计量帮助我们从数据中得出更可靠的结论。

3.Our research team decided to apply a robust statistic to ensure the results were not influenced by extreme values.

我们的研究团队决定应用一个稳健统计量以确保结果不受极端值的影响。

4.When dealing with skewed data, a robust statistic is often preferred over traditional measures.

在处理偏态数据时，通常更喜欢使用稳健统计量而不是传统度量。

5.In our analysis, we used a robust statistic to minimize the effect of outliers.

在我们的分析中，我们使用了一个稳健统计量来最小化异常值的影响。

作文

In the field of statistics, the term robust statistic refers to a type of statistical measure that remains relatively unaffected by outliers or violations of assumptions. This characteristic makes robust statistics particularly valuable in real-world data analysis where datasets often contain anomalies that can skew results. For example, if we are analyzing the average income of a group of people, a few extremely high incomes could distort the mean, making it much higher than what most individuals actually earn. In such cases, using a robust statistic like the median, which represents the middle value of a dataset, provides a more accurate reflection of the typical income. The importance of robust statistics extends beyond just income analysis. They are widely used in various fields including economics, psychology, and environmental science, where data may not always conform to normal distributions. Traditional statistical methods often rely on the assumption that data is normally distributed, which is not always the case. Therefore, applying robust statistics allows researchers to draw more reliable conclusions from their analyses. One of the key advantages of employing robust statistics is their ability to provide valid results even when the data contains outliers. Outliers are extreme values that can significantly affect the outcome of statistical calculations. For instance, in a study measuring the heights of a group of adults, if one participant is a professional basketball player who is exceptionally tall, their height could skew the average height of the group. However, using a robust statistic like the trimmed mean, which excludes a certain percentage of the highest and lowest values, can help mitigate the influence of such outliers. Moreover, robust statistics contribute to the overall integrity of research findings. By utilizing methods that are less sensitive to deviations from assumptions, researchers can confidently assert that their results are reflective of the true underlying patterns in the data. This is especially crucial in fields such as medicine, where decisions based on statistical analyses can directly affect patient care and treatment outcomes. In conclusion, the concept of robust statistic is essential for anyone engaged in data analysis. The ability to use statistical measures that withstand the influence of outliers and do not rely heavily on strict assumptions enhances the reliability and validity of research findings. As we continue to collect and analyze increasingly complex datasets, the role of robust statistics will undoubtedly become more prominent, ensuring that our interpretations of data remain grounded and accurate. Understanding and applying robust statistics is not just a technical skill but a critical component of responsible research practices that can lead to better decision-making across various domains.

在统计学领域，术语稳健统计量指的是一种统计度量，它对异常值或假设的违反保持相对不受影响。这一特性使得稳健统计量在现实世界数据分析中尤为重要，因为数据集往往包含可能扭曲结果的异常值。例如，如果我们分析一组人的平均收入，少数极高的收入可能会扭曲平均值，使其比大多数人实际赚取的要高。在这种情况下，使用稳健统计量如中位数（表示数据集中的中间值）能更准确地反映典型收入。稳健统计量的重要性不仅限于收入分析。它们广泛应用于经济学、心理学和环境科学等多个领域，这些领域的数据可能并不总是符合正态分布。因此，采用稳健统计量允许研究人员从分析中得出更可靠的结论。使用稳健统计量的一个主要优点是它们能够提供有效的结果，即使数据中包含异常值。异常值是极端值，可能显著影响统计计算的结果。例如，在一项测量一组成年人身高的研究中，如果一名参与者是一名职业篮球运动员，身高异常高，那么他们的身高可能会扭曲该组的平均身高。然而，使用像修剪均值这样的稳健统计量，可以排除一定比例的最高和最低值，从而帮助减轻此类异常值的影响。此外，稳健统计量有助于提高研究结果的整体完整性。通过利用对假设偏差不太敏感的方法，研究人员可以自信地断言他们的结果反映了数据中真实的潜在模式。这在医学等领域尤其重要，因为基于统计分析的决策可以直接影响患者护理和治疗结果。总之，稳健统计量的概念对任何从事数据分析的人来说都是至关重要的。使用能够抵御异常值影响且不依赖严格假设的统计度量的能力，提高了研究结果的可靠性和有效性。随着我们继续收集和分析日益复杂的数据集，稳健统计量的作用无疑会变得更加突出，确保我们对数据的解释保持扎实和准确。理解和应用稳健统计量不仅是一项技术技能，更是负责任的研究实践的关键组成部分，这可以在各个领域带来更好的决策。