accidental inclusion

简明释义

1. 捕虏伐; 2. 外来包伐; 3. 异源包伐; 4. 外源包伐; 5. 捕掳伐;

英英释义

例句

1.The software update caused an accidental inclusion of bugs that affected performance.

软件更新导致了一个意外包含的错误，影响了性能。

2.During the audit, we found an accidental inclusion of transactions from a previous year.

在审计过程中，我们发现了意外包含的前一年交易。

3.The report was delayed due to an accidental inclusion of outdated data.

报告因意外包含过时数据而延迟。

4.We need to correct the accidental inclusion of personal information in the public document.

我们需要纠正公共文件中意外包含的个人信息。

5.The team addressed the accidental inclusion of irrelevant images in the presentation.

团队处理了演示文稿中意外包含的不相关图像。

作文

In the realm of data analysis and research, the term accidental inclusion refers to the unintentional incorporation of irrelevant or inappropriate data into a dataset. This phenomenon can significantly skew results and lead to erroneous conclusions. For instance, imagine a study aimed at understanding the effects of a new medication on patients with a specific condition. If the researchers inadvertently include data from patients who do not have that condition, this accidental inclusion can distort the findings, making it seem as though the medication is more or less effective than it truly is. The implications of accidental inclusion extend beyond just statistical inaccuracies; they can also affect the credibility of the research. When stakeholders, such as funding bodies or regulatory agencies, rely on flawed data, the consequences can be severe. Not only might ineffective treatments be approved, but valuable resources could be wasted on further studies that build upon these erroneous foundations. Thus, ensuring the integrity of data collection processes is paramount.To mitigate the risk of accidental inclusion, researchers must adopt rigorous methodologies. This includes defining clear criteria for participant selection and employing robust data verification techniques. For example, implementing double-check systems where multiple team members review the data can help catch errors before they propagate through the analysis. Furthermore, utilizing software tools that flag inconsistencies or outliers in the data can serve as an additional layer of protection against accidental inclusion.Moreover, transparency in reporting is essential. Researchers should openly discuss the potential limitations of their studies, including any instances of accidental inclusion that may have occurred. This honesty not only enhances the reliability of the research but also fosters trust within the scientific community and among the public. When researchers acknowledge their mistakes and the possible impacts of accidental inclusion, it paves the way for improved practices in future studies.In conclusion, accidental inclusion is a critical issue that can undermine the validity of research findings. By understanding its implications and adopting preventive measures, researchers can enhance the quality of their work. As we continue to advance in various fields of study, maintaining rigorous standards in data management will be essential to ensure that our conclusions are both accurate and trustworthy. Only through diligence and transparency can we hope to minimize the risks associated with accidental inclusion and contribute meaningful knowledge to our respective fields.

在数据分析和研究领域，术语accidental inclusion指的是无意中将不相关或不适当的数据纳入数据集中。这一现象可能会显著扭曲结果并导致错误的结论。例如，想象一下一个旨在理解新药对特定疾病患者影响的研究。如果研究人员不小心包括了没有该疾病的患者的数据，那么这种accidental inclusion可能会扭曲研究结果，使得药物的效果看起来比实际情况要好或差。 accidental inclusion的影响不仅仅限于统计不准确；它们还可能影响研究的可信度。当利益相关者，例如资助机构或监管机构，依赖于有缺陷的数据时，后果可能是严重的。不仅可能批准无效的治疗方法，而且宝贵的资源也可能在建立在这些错误基础上的进一步研究中被浪费。因此，确保数据收集过程的完整性是至关重要的。为了降低accidental inclusion的风险，研究人员必须采用严格的方法论。这包括明确参与者选择的标准，并采用强有力的数据验证技术。例如，实施双重检查系统，让多个团队成员审查数据，可以帮助在分析前捕捉错误。此外，利用标记数据中的不一致性或离群值的软件工具可以作为防止accidental inclusion的额外保护层。此外，报告的透明度也是必不可少的。研究人员应公开讨论他们研究的潜在局限性，包括可能发生的任何accidental inclusion情况。这种诚实不仅增强了研究的可靠性，还在科学界和公众之间建立了信任。当研究人员承认他们的错误及其可能影响时，为未来研究中的改进实践铺平了道路。总之，accidental inclusion是一个关键问题，可能会削弱研究发现的有效性。通过理解其影响并采取预防措施，研究人员可以提高其工作的质量。随着我们在各个研究领域的不断进步，维持严格的数据管理标准对于确保我们的结论既准确又值得信赖至关重要。只有通过勤奋和透明，我们才能希望减少与accidental inclusion相关的风险，并为我们各自的领域贡献有意义的知识。