statistical uncertainty

简明释义

统计不确定性

英英释义

例句

1.The statistical uncertainty 统计不确定性 in climate models can affect policy decisions.

气候模型中的统计不确定性 统计不确定性可能影响政策决策。

2.The results of the survey showed a high level of statistical uncertainty 统计不确定性 due to the small sample size.

调查结果显示，由于样本量小，存在较高的统计不确定性 统计不确定性。

3.Researchers need to account for statistical uncertainty 统计不确定性 when interpreting their findings.

研究人员在解读其发现时需要考虑到统计不确定性 统计不确定性。

4.The report highlighted the statistical uncertainty 统计不确定性 in predicting economic growth.

报告强调了预测经济增长时的统计不确定性 统计不确定性。

5.To improve accuracy, we must reduce statistical uncertainty 统计不确定性 in our measurements.

为了提高准确性，我们必须减少测量中的统计不确定性 统计不确定性。

作文

Statistical analysis plays a crucial role in many fields, including science, economics, and social studies. One of the key concepts that researchers must grapple with is statistical uncertainty. This term refers to the inherent variability in data that arises from sampling methods, measurement errors, or natural fluctuations in the phenomena being studied. Understanding statistical uncertainty is essential for making informed decisions based on data, as it helps to quantify the reliability of the results obtained from statistical analyses.When conducting experiments or surveys, researchers often cannot collect data from an entire population due to time, cost, or logistical constraints. Instead, they rely on samples, which are subsets of the population. This is where statistical uncertainty comes into play. Because samples may not perfectly represent the population, there is a degree of uncertainty associated with any conclusions drawn from them. For example, if a survey shows that 60% of respondents support a particular policy, statistical uncertainty indicates that this figure might vary if a different sample were taken.To illustrate this concept further, consider a political poll conducted before an election. Pollsters typically report not only the percentage of support for each candidate but also a margin of error, which reflects statistical uncertainty. If Candidate A is reported to have 55% support with a margin of error of ±3%, this means that the true level of support could realistically range from 52% to 58%. Such margins help the public and decision-makers understand that while the poll provides useful insights, it is not an absolute measure of voter sentiment.Moreover, statistical uncertainty can arise from various sources, including sampling error, non-sampling error, and model uncertainty. Sampling error occurs when the sample does not accurately reflect the population, while non-sampling error can result from biases in data collection or response. Model uncertainty refers to the limitations of the statistical models used to analyze the data. All these factors contribute to the overall uncertainty in research findings, making it imperative for researchers to communicate these uncertainties effectively.In scientific research, failing to account for statistical uncertainty can lead to misguided conclusions. For instance, a medical study might find a new treatment to be effective based on a small sample size. However, if the statistical uncertainty is not acknowledged, practitioners could mistakenly believe that the treatment is universally effective, potentially leading to harmful consequences for patients. Therefore, scientists must report confidence intervals and p-values that reflect the statistical uncertainty surrounding their findings.In conclusion, statistical uncertainty is an essential aspect of data analysis that cannot be overlooked. It serves as a reminder of the limitations of our knowledge and the variability inherent in data collection. By understanding and communicating statistical uncertainty, researchers can provide a more accurate picture of their findings, allowing stakeholders to make better-informed decisions. As we continue to navigate an increasingly data-driven world, the importance of recognizing and addressing statistical uncertainty will only grow, underscoring the need for rigorous statistical practices in all fields of inquiry.

统计分析在许多领域中发挥着至关重要的作用，包括科学、经济和社会研究。研究人员必须应对的一个关键概念是统计不确定性。这个术语指的是由于抽样方法、测量误差或被研究现象的自然波动而产生的数据固有变异性。理解统计不确定性对于根据数据做出明智决策至关重要，因为它有助于量化从统计分析中获得的结果的可靠性。在进行实验或调查时，研究人员通常无法由于时间、成本或后勤限制而收集整个群体的数据。相反，他们依赖样本，即群体的子集。这就是统计不确定性发挥作用的地方。因为样本可能并不完美代表总体，所以任何从中得出的结论都存在一定程度的不确定性。例如，如果一项调查显示60%的受访者支持某项政策，统计不确定性表明如果采取不同的样本，这一数字可能会有所变化。为了进一步说明这一概念，考虑一下在选举前进行的政治民调。民调人员通常不仅报告每位候选人的支持百分比，还报告误差范围，以反映统计不确定性。如果候选人A的支持率为55%，误差范围为±3%，这意味着实际的支持水平可能在52%到58%之间。这样的误差范围帮助公众和决策者理解，虽然民调提供了有用的见解，但它并不是对选民情绪的绝对测量。此外，统计不确定性可能来自各种来源，包括抽样误差、非抽样误差和模型不确定性。抽样误差发生在样本未能准确反映总体时，而非抽样误差可能源于数据收集或响应中的偏差。模型不确定性是指用于分析数据的统计模型的局限性。所有这些因素都贡献了研究结果的整体不确定性，使研究人员必须有效地传达这些不确定性。在科学研究中，未能考虑统计不确定性可能导致错误的结论。例如，一项医学研究可能发现一种新疗法在小样本中有效。然而，如果未承认统计不确定性，从业者可能错误地认为该疗法普遍有效，可能对患者造成伤害。因此，科学家必须报告反映其发现的统计不确定性的置信区间和p值。总之，统计不确定性是数据分析中不可忽视的重要方面。它提醒我们知识的局限性和数据收集中固有的变异性。通过理解和传达统计不确定性，研究人员可以提供更准确的研究结果，使利益相关者能够做出更明智的决策。随着我们继续在一个日益数据驱动的世界中航行，认识和解决统计不确定性的重要性只会增加，强调了在所有研究领域中严格统计实践的必要性。