biased estimate

简明释义

有偏估计

英英释义

例句

1.Using outdated data can lead to a biased estimate of future trends.

使用过时数据可能导致对未来趋势的偏差估计。

2.The model produced a biased estimate because it did not account for all variables.

该模型产生了一个偏差估计，因为它没有考虑所有变量。

3.The financial analyst warned that the biased estimate could mislead investors.

金融分析师警告说，这个偏差估计可能会误导投资者。

4.In scientific research, it's crucial to avoid a biased estimate to ensure valid results.

在科学研究中，避免偏差估计至关重要，以确保结果有效。

5.The survey results provided a biased estimate of the population's preferences due to the small sample size.

由于样本量小，调查结果提供了一个偏差估计人群偏好的结果。

作文

In the field of statistics, the concept of a biased estimate is crucial for understanding the reliability and validity of data analysis. A biased estimate refers to an estimation that systematically deviates from the true value of the parameter being measured. This deviation can arise from various sources, including sampling errors, measurement errors, or even the methodology used in data collection. Understanding what constitutes a biased estimate is essential for researchers and analysts who aim to draw accurate conclusions from their data.For instance, consider a scenario where a researcher is conducting a survey to determine the average height of adult males in a city. If the researcher only surveys individuals at a basketball game, the resulting estimate of average height will likely be a biased estimate because it does not represent the entire population of adult males in the city. This type of bias occurs because the sample is not randomly selected; instead, it is skewed towards taller individuals who are more likely to attend such events.The implications of relying on a biased estimate can be significant. If decisions are made based on faulty data, it can lead to ineffective policies, misallocation of resources, and ultimately, negative outcomes for the intended population. For example, if city planners were to use the biased estimate of average male height from the basketball game to design public facilities, they might create spaces that do not accommodate the average individual, leading to discomfort and dissatisfaction among the general public.To mitigate the risk of obtaining a biased estimate, researchers must employ rigorous sampling techniques. Random sampling, stratified sampling, and ensuring diversity in the sample population are all strategies that can help produce more accurate estimates. Additionally, researchers should be aware of potential biases in their measurement tools and methods. For instance, if a survey question is leading or ambiguous, it can skew responses and result in a biased estimate.Moreover, it is important for analysts to report confidence intervals and margins of error alongside their estimates. This practice provides context and helps users of the data understand the potential variability and reliability of the findings. By acknowledging the possibility of a biased estimate, researchers can foster transparency and trust in their work.In conclusion, understanding the concept of a biased estimate is vital for anyone involved in data analysis. It serves as a reminder of the importance of careful methodology and the need for critical thinking when interpreting statistical results. By recognizing and addressing potential biases, researchers can improve the quality of their estimates and contribute to more informed decision-making processes. Ultimately, the goal should always be to strive for accuracy and reliability in data representation to better serve society's needs.

在统计学领域，偏差估计的概念对于理解数据分析的可靠性和有效性至关重要。偏差估计是指一种系统性偏离所测量参数真实值的估计。这种偏差可能来自各种来源，包括抽样误差、测量误差，甚至数据收集方法。理解什么构成偏差估计对于希望从数据中得出准确结论的研究人员和分析师来说是必不可少的。例如，考虑一个场景，研究人员正在进行调查，以确定某城市成年男性的平均身高。如果研究人员仅在篮球比赛中调查个人，所得出的成年男性平均身高的估计将很可能是偏差估计，因为它并不代表该城市成年男性的整个群体。这种偏差发生是因为样本不是随机选择的；相反，它偏向于更高个子的个体，这些个体更有可能参加这样的活动。依赖于偏差估计的后果可能是重大的。如果基于错误的数据做出决策，可能导致无效的政策、资源的不当分配，最终对目标人群产生负面影响。例如，如果城市规划者使用篮球比赛的偏差估计作为成年男性的平均身高来设计公共设施，他们可能会创建不适合普通人的空间，导致公众的不适和不满。为了降低获得偏差估计的风险，研究人员必须采用严格的抽样技术。随机抽样、分层抽样以及确保样本人群的多样性都是可以帮助产生更准确估计的策略。此外，研究人员还应注意测量工具和方法中的潜在偏差。例如，如果调查问题具有引导性或模糊性，可能会扭曲回应，从而导致偏差估计。此外，分析师在报告估计时应同时提供置信区间和误差范围。这种做法提供了背景信息，并帮助数据用户理解发现的潜在变异性和可靠性。通过承认偏差估计的可能性，研究人员可以促进其工作的透明度和信任。总之，理解偏差估计的概念对于任何参与数据分析的人来说都是至关重要的。它提醒我们关注方法的严谨性以及在解释统计结果时需要批判性思维。通过识别和解决潜在偏差，研究人员可以提高估计的质量，并为更明智的决策过程做出贡献。最终，目标应该始终是努力追求数据表示的准确性和可靠性，以更好地满足社会的需求。