skew

简明释义

英[skjuː]美[skjuː]

v. 偏离，歪斜；歪曲，使有误差；使曲解，使存有偏见；（统计）使（分布）偏态，使不匀称；斜视

adj. 歪斜的；（数）异面的，（曲线）不在一个平面的；（统计）偏态的

n. 斜角，倾斜；（对特定人或对象的）偏见；（统计）偏态，偏斜性

第 三 人 称 单 数 s k e w s

现 在 分 词 s k e w i n g

过 去 式 s k e w e d

过 去 分 词 s k e w e d

英英释义

单词用法

同义词

反义词

例句

1.Countries that import many intermediate goods for processing and re-export skew the percentages.

那些进口半成品，经加工后再次出口的国家扭曲了这一比例。

2.The conceit and the language skew a little older, so this is a nifty book to read with your kids.

书中巧妙的想象和语言有一点倾向于年龄大点的人，因此这是一本很好的可以和孩子一起读的书。

3.The estimate could be incorrect if there is data skew on the column.

如果列上有数据偏离，评估结果就可能不正确。

4.Because of this, it's difficult to parse through other factors that would skew the results.

正因为如此，很难将其他影响结果的因素剖析开来。

5.It is important that the test and training documents do not overlap, which could skew the results.

需要确保测试和训练文件没有重合，否则会造成结果不准确。

6.A few outliers skew the data, they say.

他们表示：少数外行曲解了研究数据。

7.The angle of the roof is skewed, which affects the overall design of the house.

屋顶的角度是倾斜的，这影响了房子的整体设计。

8.His opinion seemed skewed by personal bias rather than objective facts.

他的观点似乎由于个人偏见而被偏斜，而不是基于客观事实。

9.The results of the survey were skewed because only a small group of people participated.

调查结果因只有少数人参与而被偏斜。

10.In statistics, a skew can indicate whether a distribution is symmetrical or asymmetrical.

在统计学中，偏斜可以指示一个分布是对称的还是不对称的。

11.The data was skewed towards higher income levels, making it less representative of the general population.

数据向高收入水平偏斜，使其对一般人群的代表性降低。

作文

In today's world, data plays a crucial role in decision-making processes across various fields, from business to healthcare. However, the interpretation of this data can often be skewed due to several factors. Understanding how and why data can be skewed is essential for anyone who relies on statistics to inform their choices. Skew refers to a distortion or asymmetry in a dataset that can lead to misleading conclusions. When data is skewed, it does not accurately represent the true nature of the phenomenon being studied. For example, consider a scenario where a company wants to analyze employee satisfaction based on survey results. If the majority of respondents are from the management level, the results may be skewed towards a more positive outlook, as managers might have a different perspective on job satisfaction compared to entry-level employees. This skew can result in decisions that do not address the actual concerns of the workforce, ultimately leading to dissatisfaction and high turnover rates. Moreover, skewness can occur in various forms, such as positive skew (where the tail on the right side of the distribution is longer or fatter) or negative skew (where the tail on the left side is longer or fatter). This statistical property is significant because it impacts how we interpret averages and medians. For instance, in a positively skewed distribution, the mean will be greater than the median, which could mislead stakeholders into thinking that the average experience is better than it actually is. It is also important to recognize that skew can arise from sampling errors or biases in data collection methods. If a survey is conducted online, for example, individuals without internet access are excluded, potentially leading to a skewed representation of the population. Similarly, if the questions posed in a survey are leading or biased, the responses can reflect that bias, resulting in skewed data. To mitigate the effects of skew, researchers and analysts must employ rigorous methodologies. This includes ensuring a representative sample size, using random sampling techniques, and being mindful of question phrasing in surveys. Additionally, employing statistical techniques to adjust for skewness can help provide a clearer picture of the data. For example, transformations such as logarithmic or square root can be applied to skewed data to normalize it, making it easier to analyze and interpret. In conclusion, understanding the concept of skew is vital for accurate data analysis and interpretation. Whether in business, healthcare, or any other field that relies on data, recognizing the potential for skewness allows decision-makers to approach their analyses with caution. By addressing skew in data collection and analysis, we can make more informed decisions that truly reflect the experiences and needs of the populations we serve.

在当今世界，数据在各个领域的决策过程中发挥着至关重要的作用，从商业到医疗保健。然而，由于多种因素，这些数据的解释可能会出现偏斜。理解数据为何以及如何可能被偏斜对于任何依赖统计数据来指导选择的人来说都是至关重要的。偏斜指的是数据集中的扭曲或不对称，这可能导致误导性的结论。当数据被偏斜时，它并不能准确地代表所研究现象的真实本质。例如，考虑一个公司希望根据调查结果分析员工满意度的情境。如果大多数受访者来自管理层，那么结果可能会偏向于更积极的观点，因为经理们可能与初级员工对工作满意度有不同的看法。这种偏斜可能导致做出的决策未能解决员工的实际担忧，最终导致不满和高离职率。此外，偏斜可以以多种形式出现，例如正偏斜（分布右侧的尾部较长或较胖）或负偏斜（左侧的尾部较长或较胖）。这一统计特性是重要的，因为它影响我们如何解释平均值和中位数。例如，在正偏斜的分布中，均值将大于中位数，这可能会误导利益相关者认为平均体验比实际情况要好。还需要认识到，偏斜可能源于抽样错误或数据收集方法中的偏见。如果调查是在网上进行的，例如，没有互联网接入的个人就会被排除在外，这可能导致对总体的偏斜表示。同样，如果调查中提出的问题具有引导性或偏见，回应也可能反映出这种偏见，从而导致数据的偏斜。为了减轻偏斜的影响，研究人员和分析师必须采用严格的方法。这包括确保具有代表性的样本大小，使用随机抽样技术，并注意调查中问题的措辞。此外，采用统计技术来调整偏斜性可以帮助提供更清晰的数据图景。例如，可以对偏斜的数据应用对数或平方根等变换，以使其规范化，从而更易于分析和解释。总之，理解偏斜的概念对于准确的数据分析和解释至关重要。无论是在商业、医疗保健还是任何其他依赖数据的领域，认识到偏斜的潜力使决策者能够谨慎地处理他们的分析。通过解决数据收集和分析中的偏斜，我们可以做出更明智的决策，真正反映我们服务的人群的经验和需求。