single sample

简明释义

一次抽样样品

英英释义

例句

1.In a clinical trial, researchers often start with a single sample 单个样本 to test the effectiveness of a new drug.

在临床试验中，研究人员通常先用一个single sample 单个样本 来测试新药的有效性。

2.A single sample 单个样本 from the batch was tested for quality assurance.

从这批产品中抽取了一个single sample 单个样本 进行质量保证测试。

3.The scientist collected a single sample 单个样本 of soil to analyze its nutrient content.

科学家收集了一个single sample 单个样本 的土壤来分析其营养成分。

4.In statistics, drawing a single sample 单个样本 from a larger population is crucial for making inferences.

在统计学中，从较大群体中抽取一个single sample 单个样本 对于做出推断至关重要。

5.When analyzing data, a single sample 单个样本 can provide insight but may not represent the entire population.

在分析数据时，一个single sample 单个样本 可以提供见解，但可能无法代表整个群体。

作文

In the field of statistics, the term single sample refers to a set of data collected from a single group or individual for the purpose of analysis. This concept is essential in various research areas, including psychology, medicine, and market research. A single sample can provide insights into the characteristics or behaviors of a specific population without the need for extensive data collection from multiple groups. For instance, if a researcher wants to understand the eating habits of college students, they might collect a single sample of 100 students from a particular university. This focused approach allows for detailed analysis while minimizing the complexity that comes with larger datasets.However, relying on a single sample also has its limitations. One significant drawback is that the findings may not be generalizable to the broader population. If the sample is too small or not representative, the conclusions drawn may be skewed or inaccurate. Therefore, researchers must carefully consider their sampling methods and ensure that the single sample they choose adequately reflects the diversity of the target population.Moreover, the use of a single sample can lead to overfitting in certain statistical models. Overfitting occurs when a model describes random error or noise instead of the underlying relationship. When working with a single sample, there is a risk that the results may appear significant due to the peculiarities of that particular dataset rather than any real-world implications. To mitigate this risk, researchers often employ techniques such as cross-validation, where the single sample is divided into subsets to validate the findings against different portions of the data.In contrast, using multiple samples can provide a more comprehensive view of the research question. By comparing data from various groups, researchers can identify trends and patterns that may not be visible in a single sample. This approach enhances the reliability of the results and allows for a more nuanced understanding of the topic at hand. For example, if the same researcher studying college students were to gather data from multiple universities across different regions, they would likely obtain a richer dataset that reflects varying cultural and social influences on eating habits.In conclusion, a single sample serves as a valuable tool in research, offering a straightforward way to gather and analyze data. However, it is crucial for researchers to acknowledge the limitations associated with this method. While a single sample can yield important insights, it should ideally be complemented by additional data sources or methods to ensure robust and reliable conclusions. By striking a balance between simplicity and comprehensiveness, researchers can effectively harness the power of a single sample while minimizing the risks of bias and overfitting. Ultimately, the goal is to contribute meaningful knowledge to the field while maintaining scientific rigor and integrity.

在统计学领域，术语single sample指的是从单一群体或个体收集的数据集，以便进行分析。这个概念在心理学、医学和市场研究等多个研究领域中至关重要。single sample可以提供对特定人群特征或行为的洞察，而无需从多个群体进行广泛的数据收集。例如，如果研究者想了解大学生的饮食习惯，他们可能会从某个特定大学收集100名学生的single sample。这种集中化的方法允许进行详细分析，同时减少了大数据集带来的复杂性。然而，依赖于single sample也有其局限性。一个显著的缺点是研究结果可能无法推广到更广泛的人群。如果样本过小或不具代表性，得出的结论可能会偏差或不准确。因此，研究者必须仔细考虑他们的抽样方法，并确保所选择的single sample能够充分反映目标人群的多样性。此外，使用single sample可能导致某些统计模型中的过拟合。过拟合发生在模型描述随机误差或噪声而不是潜在关系时。当处理single sample时，结果可能由于该特定数据集的特殊性而显得显著，而不是任何现实世界的影响。为了减轻这种风险，研究者通常采用交叉验证等技术，即将single sample划分为子集，以验证不同数据部分的发现。相比之下，使用多个样本可以提供对研究问题的更全面的视角。通过比较来自不同群体的数据，研究者可以识别在single sample中可能不可见的趋势和模式。这种方法增强了结果的可靠性，并允许对手头主题进行更细致的理解。例如，如果同一位研究大学生的研究者从不同地区的多所大学收集数据，他们很可能会获得一个更丰富的数据集，反映出饮食习惯受到的文化和社会影响的差异。总之，single sample作为研究中的一种有价值工具，提供了一种直接收集和分析数据的方法。然而，研究者必须承认与这种方法相关的局限性。虽然single sample可以产生重要的洞察，但理想情况下应补充额外的数据来源或方法，以确保得出的结论稳健可靠。通过在简约与全面之间取得平衡，研究者可以有效利用single sample的力量，同时最小化偏差和过拟合的风险。最终目标是在保持科学严谨性和诚信的同时，为该领域贡献有意义的知识。