analytic uncertainty

简明释义

1. 分析的不确定性; 2. 分析误差;

英英释义

例句

1.The researchers reported that the analytic uncertainty 分析不确定性 in their measurements could affect the overall conclusions of the study.

研究人员报告称，他们测量中的分析不确定性分析不确定性可能会影响研究的整体结论。

2.In environmental studies, understanding analytic uncertainty 分析不确定性 is crucial for accurate risk assessments.

在环境研究中，理解分析不确定性分析不确定性对准确的风险评估至关重要。

3.The team used statistical methods to quantify the analytic uncertainty 分析不确定性 associated with their data.

团队使用统计方法量化了与他们数据相关的分析不确定性分析不确定性。

4.Before making policy decisions, it is important to evaluate the analytic uncertainty 分析不确定性 in economic forecasts.

在做出政策决定之前，评估经济预测中的分析不确定性分析不确定性是很重要的。

5.The report highlighted the analytic uncertainty 分析不确定性 in climate models, which can lead to varying predictions.

报告强调了气候模型中的分析不确定性分析不确定性，这可能导致不同的预测结果。

作文

In the realm of scientific research and data analysis, the concept of analytic uncertainty plays a crucial role in understanding the reliability and validity of the results obtained. Analytic uncertainty refers to the inherent variability and potential errors associated with the measurement and analysis of data. This uncertainty can arise from various sources, including instrument limitations, environmental factors, and human error. Therefore, recognizing and quantifying analytic uncertainty is essential for drawing meaningful conclusions from experimental data.To illustrate the importance of analytic uncertainty, consider a scenario in which a team of researchers is investigating the effects of a new drug on blood pressure levels. They conduct a series of experiments and collect data on the blood pressure readings of participants before and after administering the drug. However, due to differences in individual physiology, variations in measurement techniques, and the influence of external factors like stress or diet, the readings may vary significantly among participants. Here, analytic uncertainty comes into play, as it highlights the need to account for these variations when interpreting the results.One common method to address analytic uncertainty is through statistical analysis. By applying statistical tools, researchers can estimate the degree of uncertainty associated with their measurements. This often involves calculating confidence intervals, which provide a range within which the true value is likely to fall. For example, if the average reduction in blood pressure is reported as 10 mmHg with a confidence interval of ±2 mmHg, this indicates that the researchers are 95% confident that the true average reduction lies between 8 mmHg and 12 mmHg. Such statistical measures help to convey the level of analytic uncertainty and allow for more informed decision-making based on the results.Moreover, the communication of analytic uncertainty is vital in the context of policy-making and public health. When research findings are presented to policymakers, it is crucial to convey not only the outcomes but also the uncertainty surrounding those outcomes. For instance, if a study suggests that a particular intervention reduces the incidence of a disease by 30%, but the analytic uncertainty indicates a wide range of possible outcomes, policymakers must carefully consider the implications of acting on this information. Ignoring analytic uncertainty could lead to misguided decisions that may adversely affect public health.Furthermore, advancements in technology and methodology have enhanced our ability to quantify analytic uncertainty. With the advent of sophisticated analytical tools and computational models, researchers can now simulate various scenarios and assess how different factors contribute to uncertainty. This enables a more comprehensive understanding of how analytic uncertainty influences research outcomes and helps in refining experimental designs to minimize uncertainty in future studies.In conclusion, analytic uncertainty is a fundamental aspect of scientific research that cannot be overlooked. It encompasses the variability and potential errors in data analysis, influencing the interpretation of results and subsequent decision-making processes. By acknowledging and addressing analytic uncertainty, researchers can enhance the credibility of their findings and contribute to more effective policies and practices in various fields. As we continue to advance in our research endeavors, fostering a deeper understanding of analytic uncertainty will be essential in ensuring that our conclusions are both reliable and actionable.

在科学研究和数据分析的领域中，“分析不确定性”这一概念在理解获得结果的可靠性和有效性方面发挥着至关重要的作用。“分析不确定性”指的是与数据测量和分析相关的固有变异性和潜在误差。这种不确定性可能源于多种因素，包括仪器限制、环境因素和人为错误。因此，认识到并量化“分析不确定性”对于从实验数据中得出有意义的结论至关重要。为了说明“分析不确定性”的重要性，考虑一个场景，其中一组研究人员正在调查一种新药对血压水平的影响。他们进行了一系列实验，并收集了参与者在服用药物前后的血压读数。然而，由于个体生理差异、测量技术的变化以及压力或饮食等外部因素的影响，参与者之间的读数可能会显著不同。在这里，“分析不确定性”就发挥了作用，因为它强调了在解释结果时需要考虑这些变化。解决“分析不确定性”的一种常见方法是通过统计分析。通过应用统计工具，研究人员可以估计与其测量相关的不确定程度。这通常涉及计算置信区间，提供一个真实值可能落入的范围。例如，如果报告的平均血压降低为10 mmHg，置信区间为±2 mmHg，这表明研究人员有95%的信心认为真实的平均降低值在8 mmHg和12 mmHg之间。这样的统计措施有助于传达“分析不确定性”的水平，并允许基于结果做出更明智的决策。此外，在政策制定和公共卫生的背景下，传播“分析不确定性”至关重要。当研究结果呈现给政策制定者时，传达不仅是结果，还有围绕这些结果的不确定性是至关重要的。例如，如果一项研究表明某种干预措施降低了疾病发生率30%，但“分析不确定性”表明可能的结果范围很广，则政策制定者必须仔细考虑依据这些信息采取行动的影响。忽视“分析不确定性”可能导致误导性的决策，可能对公共健康产生不利影响。此外，技术和方法的进步增强了我们量化“分析不确定性”的能力。随着复杂分析工具和计算模型的出现，研究人员现在可以模拟各种情景，并评估不同因素如何影响不确定性。这使得我们能够更全面地理解“分析不确定性”如何影响研究结果，并帮助改进实验设计，以最小化未来研究中的不确定性。总之，“分析不确定性”是科学研究中不可忽视的基本方面。它涵盖了数据分析中的变异性和潜在误差，影响结果的解释和随后的决策过程。通过承认和解决“分析不确定性”，研究人员可以增强其发现的可信度，并为各个领域的更有效政策和实践做出贡献。随着我们在研究工作中的不断推进，深入理解“分析不确定性”将对确保我们的结论既可靠又可行至关重要。