subsamples

简明释义

英[ˈsʌbˌsɑːmplz]美[ˈsʌbˌsæmpəlz]

n. 附属样本；[数]次级样本（subsample 的复数）

英英释义

单词用法

同义词

反义词

例句

1.Used for the printing & dyeing industry to pad subsamples, and applies to cotton, T/C and other fabrics.

用于印染厂长车、卷染生产行业而设计的小样轧染试色使用，并适用于棉、T/C及其它织物；

2.Quality measurements were made on subsamples of black point-affected and black point-free grain from 5 wheat samples.

对5个小麦品种有黑胚病症状籽粒和无黑胚病症状籽粒的样品分别进行品质分析。

3.Quality measurements were made on subsamples of black point-affected and black point-free grain from 5 wheat samples.

对5个小麦品种有黑胚病症状籽粒和无黑胚病症状籽粒的样品分别进行品质分析。

4.Each subsample was analyzed separately to identify any variations in results.

每个子样本都被单独分析以识别结果中的任何变化。

5.The researchers collected multiple subsamples from the main data set to ensure accuracy.

研究人员从主数据集中收集了多个子样本以确保准确性。

6.To validate the findings, the team compared the subsamples with the original samples.

为了验证发现，团队将子样本与原始样本进行了比较。

7.In the experiment, we took subsamples at different time intervals to monitor changes.

在实验中，我们在不同的时间间隔取了子样本以监测变化。

8.The environmental study used subsamples from various locations to understand regional differences.

环境研究使用了来自不同地点的子样本以了解区域差异。

作文

In the field of statistics and research, the concept of subsamples is crucial for understanding how to analyze data effectively. A subsample refers to a smaller portion of a larger sample that is selected for analysis. This method allows researchers to draw conclusions about a larger population without the need to study every single member of that population, which can often be impractical or impossible. By examining subsamples, researchers can save time and resources while still obtaining valuable insights.For instance, consider a scenario where a company wants to understand customer satisfaction across its various products. Instead of surveying every single customer, which could be logistically challenging and expensive, the company can select subsamples of customers from different regions or demographics. This approach enables them to gather feedback that is representative of the larger customer base.The process of selecting subsamples must be done carefully to ensure that it accurately reflects the diversity of the entire population. Random sampling techniques are often employed to minimize bias and increase the reliability of the results. When the subsamples are properly selected, they can provide insights that are generalizable to the broader group.Another important aspect of using subsamples is the ability to conduct experiments or tests in a more controlled environment. For example, in medical research, scientists may use subsamples of participants to test the efficacy of a new drug. By focusing on a smaller group, researchers can monitor side effects and outcomes more closely, leading to more accurate results. Once the drug's effectiveness is established within the subsamples, it can then be applied to larger populations with greater confidence.However, relying solely on subsamples can also introduce some challenges. If the subsamples are not representative of the overall population, the conclusions drawn from the analysis may lead to incorrect assumptions. Therefore, it is essential for researchers to report their sampling methods and acknowledge any limitations associated with their subsamples. Transparency in research enhances credibility and allows others to assess the validity of the findings.In conclusion, subsamples play a vital role in research methodologies across various fields. They enable researchers to efficiently gather data, conduct experiments, and draw meaningful conclusions while managing resources effectively. By understanding the significance of subsamples and employing sound sampling techniques, researchers can enhance the quality of their studies and contribute valuable knowledge to their respective disciplines. As we continue to navigate an increasingly data-driven world, the importance of mastering the concept of subsamples cannot be overstated, as it paves the way for informed decision-making and innovation in research practices.

在统计学和研究领域，subsamples的概念对于有效分析数据至关重要。subsample指的是从更大样本中选出的较小部分，用于分析。这种方法使研究人员能够在不需要研究整个群体的每一个成员的情况下，对更大的人群得出结论，这通常是不切实际或不可能的。通过检查subsamples，研究人员可以节省时间和资源，同时仍然获得有价值的见解。例如，考虑一个公司想要了解其各种产品的客户满意度的场景。该公司可以选择来自不同地区或人口统计的subsamples客户进行调查，而不是调查每一位客户，这在后勤上可能是具有挑战性的且昂贵的。这种方法使他们能够收集代表更大客户基础的反馈。选择subsamples的过程必须谨慎进行，以确保它准确反映整个群体的多样性。通常采用随机抽样技术，以最小化偏差并提高结果的可靠性。当subsamples被正确选择时，它们可以提供可推广到更广泛群体的见解。使用subsamples的另一个重要方面是能够在更受控的环境中进行实验或测试。例如，在医学研究中，科学家可能会使用参与者的subsamples来测试新药的有效性。通过集中于较小的群体，研究人员可以更密切地监测副作用和结果，从而得出更准确的结果。一旦在subsamples中确立了药物的有效性，就可以更有信心地将其应用于更大的人群。然而，仅依赖subsamples也可能带来一些挑战。如果subsamples不能代表整体人群，那么从分析中得出的结论可能会导致错误的假设。因此，研究人员必须报告他们的抽样方法，并承认与其subsamples相关的任何局限性。研究透明度增强了可信度，使其他人能够评估研究发现的有效性。总之，subsamples在各个领域的研究方法中发挥着至关重要的作用。它们使研究人员能够高效地收集数据、进行实验并得出有意义的结论，同时有效管理资源。通过理解subsamples的重要性并采用合理的抽样技术，研究人员可以提高研究的质量，并为各自学科贡献有价值的知识。随着我们继续在数据驱动的世界中导航，掌握subsamples的概念的重要性不容低估，因为它为研究实践中的明智决策和创新铺平了道路。