observational data

简明释义

观测记录

英英释义

例句

1.The observational data indicated a significant correlation between diet and health outcomes.

这些观察性数据表明饮食与健康结果之间存在显著相关性。

2.In this study, we utilized observational data to track patient outcomes over a five-year period.

在这项研究中，我们利用了观察性数据来追踪患者在五年内的结果。

3.By analyzing observational data, scientists can identify patterns that may not be visible in experimental studies.

通过分析观察性数据，科学家可以识别出在实验研究中可能看不出的模式。

4.The team used observational data to assess the impact of urbanization on local wildlife.

团队使用观察性数据评估城市化对当地野生动物的影响。

5.The researchers collected observational data from various sources to analyze the effects of climate change.

研究人员从各种来源收集了观察性数据以分析气候变化的影响。

作文

In the realm of research, particularly in the fields of social sciences and epidemiology, the term observational data refers to information collected through direct observation rather than through experimental manipulation. This type of data is crucial for understanding real-world scenarios where controlled experiments may not be feasible or ethical. For instance, consider a study investigating the effects of air pollution on public health. Researchers may collect observational data by monitoring hospital admissions in areas with varying levels of pollution over time, rather than randomly assigning individuals to polluted or non-polluted environments, which would be impractical and unethical.The strength of observational data lies in its ability to reflect actual conditions and behaviors as they occur in natural settings. This allows researchers to gain insights into complex phenomena that are often influenced by multiple factors. However, one must also be cautious when interpreting observational data, as it can be susceptible to biases and confounding variables. For example, if researchers observe a correlation between high levels of air pollution and increased hospital admissions, they must consider other factors such as socioeconomic status, pre-existing health conditions, and access to healthcare that could also influence these outcomes.To mitigate these challenges, researchers often employ various statistical techniques to analyze observational data. Techniques such as regression analysis, propensity score matching, and instrumental variable analysis help to adjust for potential confounders, allowing for a more accurate interpretation of the data. Despite these methods, it is important to acknowledge that causation cannot be definitively established through observational data alone; instead, it can suggest associations that warrant further investigation.Moreover, observational data plays a significant role in generating hypotheses that can later be tested through experimental studies. For instance, if researchers observe a pattern suggesting that increased physical activity is associated with lower rates of depression, this finding can lead to further experimental studies designed to explore the causal relationship between exercise and mental health. In this way, observational data serves as a valuable foundation for scientific inquiry, guiding researchers toward questions that are worth exploring in greater depth.In conclusion, while observational data provides essential insights into real-world phenomena, it is imperative for researchers to approach its analysis with caution. Understanding the limitations and potential biases inherent in observational data is crucial for drawing valid conclusions. As we continue to advance in our research methodologies, the integration of observational data with experimental findings will enhance our understanding of complex issues and contribute to evidence-based decision-making in various fields, from public health to policy development.

在研究领域，尤其是社会科学和流行病学中，术语observational data指的是通过直接观察而非实验操控收集的信息。这种数据对于理解现实世界的场景至关重要，因为在这些场景中，控制实验可能不可行或不道德。例如，考虑一个研究空气污染对公众健康影响的研究。研究人员可以通过监测在不同污染水平地区的医院入院情况来收集observational data，而不是随机分配个体到污染或非污染环境，这在实际操作中既不切实际也不符合伦理。observational data的强大之处在于它能够反映自然环境中发生的实际条件和行为。这使得研究人员能够深入了解受多种因素影响的复杂现象。然而，在解释observational data时，必须谨慎，因为它可能受到偏见和混杂变量的影响。例如，如果研究人员观察到高水平空气污染与医院入院率增加之间存在相关性，他们必须考虑其他因素，如社会经济状况、既往健康状况以及医疗服务的可及性，这些因素也可能影响这些结果。为了减轻这些挑战，研究人员通常采用各种统计技术来分析observational data。回归分析、倾向评分匹配和工具变量分析等技术有助于调整潜在的混杂因素，从而允许对数据进行更准确的解释。尽管有这些方法，但重要的是要承认，仅通过observational data无法明确建立因果关系；相反，它可以提示值得进一步研究的关联。此外，observational data在生成假设方面发挥着重要作用，这些假设可以通过实验研究进行后续测试。例如，如果研究人员观察到增加的体育活动与抑郁症发生率降低之间存在模式，这一发现可以引导进一步的实验研究，旨在探讨运动与心理健康之间的因果关系。通过这种方式，observational data为科学探究提供了宝贵的基础，引导研究人员提出值得深入探索的问题。总之，虽然observational data提供了对现实现象的重要见解，但研究人员在分析其时必须谨慎。理解observational data固有的局限性和潜在偏见对于得出有效结论至关重要。随着我们研究方法的不断进步，将observational data与实验发现结合起来将增强我们对复杂问题的理解，并有助于在公共卫生到政策制定等各个领域进行基于证据的决策。