dry analysis

简明释义

干法分析

英英释义

例句

1.In the meeting, we discussed the dry analysis 乏味的分析 of last quarter's sales figures without any engaging visuals.

在会议上，我们讨论了上个季度销售数据的dry analysis 乏味的分析，没有任何引人入胜的视觉效果。

2.While the dry analysis 乏味的分析 is important, it's essential to present it in a more dynamic way to capture the audience's attention.

虽然dry analysis 乏味的分析很重要，但以更动态的方式呈现它以吸引观众的注意力是至关重要的。

3.Despite the dry analysis 乏味的分析, the findings were crucial for making informed decisions.

尽管有些dry analysis 乏味的分析，但这些发现对做出明智的决策至关重要。

4.The report was filled with numbers and statistics, making it a bit of a dry analysis 乏味的分析 for those not interested in data.

这份报告充满了数字和统计数据，对于那些对数据不感兴趣的人来说，简直是个dry analysis 乏味的分析。

5.The professor warned us that the upcoming lecture would be a dry analysis 乏味的分析 of economic theories.

教授警告我们即将到来的讲座将是对经济理论的dry analysis 乏味的分析。

作文

In the realm of academic research and data evaluation, the term dry analysis is often utilized to describe a methodical approach that prioritizes objectivity and precision over emotional or subjective interpretation. This analytical style is characterized by its reliance on quantitative data and statistical methods, which can sometimes lead to conclusions that lack a narrative or personal touch. While this may be beneficial in certain contexts, it can also result in findings that feel disconnected from the real-world implications they represent.For instance, consider a study that examines the effects of a new educational program on student performance. A dry analysis of the data might reveal that students who participated in the program scored an average of 10 points higher on standardized tests compared to those who did not. While this statistic is valuable, it does not convey the experiences of the students involved or the challenges they faced. The lack of qualitative insights can leave stakeholders with an incomplete understanding of the program's impact.Furthermore, relying solely on dry analysis can lead to oversimplified conclusions. In the case of public health studies, for example, raw numbers may indicate a decrease in disease prevalence without addressing underlying factors such as socioeconomic status, access to healthcare, or cultural attitudes towards treatment. Without considering these nuances, policymakers may implement strategies that fail to address the root causes of health disparities.On the other hand, incorporating qualitative research alongside dry analysis can enrich our understanding of complex issues. By blending numerical data with personal narratives, researchers can paint a more comprehensive picture that resonates with both experts and the general public. For instance, interviews with students from the earlier example could reveal how the educational program not only improved test scores but also fostered a sense of community and belonging among participants.Moreover, the integration of qualitative insights into the analytical process can enhance the credibility and applicability of research findings. When stakeholders see the human side of data, they are more likely to engage with the results and take action based on them. This is particularly important in fields such as social sciences and healthcare, where the stakes are high and the consequences of decisions can profoundly affect individuals' lives.In conclusion, while dry analysis plays a crucial role in ensuring the integrity and accuracy of research, it is essential to recognize its limitations. By complementing quantitative data with qualitative insights, researchers can provide a more holistic view of the issues at hand. This balanced approach not only enriches the analytical process but also fosters a deeper connection between the data and the real-world situations it seeks to illuminate. Ultimately, embracing both the rigor of dry analysis and the richness of qualitative research can lead to more informed decision-making and positive outcomes in various fields.

在学术研究和数据评估的领域中，术语干巴巴的分析常用于描述一种方法论，它优先考虑客观性和精确性，而不是情感或主观解释。这种分析风格的特点是依赖于定量数据和统计方法，这有时会导致结论缺乏叙事性或个人色彩。虽然在某些情况下这可能是有益的，但它也可能导致发现与其所代表的现实世界影响脱节。例如，考虑一项研究，考察一个新的教育项目对学生表现的影响。对数据的干巴巴的分析可能会揭示参与该项目的学生在标准化测试中比未参与的学生平均高出10分。虽然这一统计数据是有价值的，但它并没有传达参与学生的经历或他们面临的挑战。缺乏定性洞察可能使利益相关者对该项目的影响有不完整的理解。此外，仅仅依赖干巴巴的分析可能导致过于简化的结论。在公共卫生研究的案例中，原始数字可能表明疾病流行率下降，而没有解决潜在因素，例如社会经济地位、医疗保健的可及性或对治疗的文化态度。如果不考虑这些细微差别，政策制定者可能会实施无法解决健康差异根本原因的策略。另一方面，将定性研究与干巴巴的分析结合起来可以丰富我们对复杂问题的理解。通过将数据信息与个人叙述相结合，研究人员可以描绘出更全面的图景，使专家和公众都能产生共鸣。例如，早期例子中的学生访谈可以揭示教育项目不仅提高了考试成绩，还在参与者之间培养了社区感和归属感。此外，将定性洞察融入分析过程可以增强研究发现的可信度和适用性。当利益相关者看到数据的人性化一面时，他们更有可能参与结果并基于此采取行动。这在社会科学和医疗保健等领域尤为重要，因为这些领域的风险很高，决策的后果可能深刻影响个人的生活。总之，虽然干巴巴的分析在确保研究的完整性和准确性方面发挥着至关重要的作用，但重要的是要认识到它的局限性。通过将定量数据与定性洞察相结合，研究人员可以提供对当前问题的更全面的视角。这种平衡的方法不仅丰富了分析过程，还促进了数据与其试图阐明的现实情况之间的更深连接。最终，拥抱干巴巴的分析的严格性与定性研究的丰富性，可以在各个领域带来更明智的决策和积极的结果。