random sampling

简明释义

随机抽样

英英释义

例句

1.In a quality control process, random sampling is used to check product defects at various stages of production.

在质量控制过程中，使用随机抽样来检查生产各个阶段的产品缺陷。

2.The researchers used random sampling to ensure that every participant had an equal chance of being selected for the study.

研究人员使用随机抽样以确保每位参与者都有相等的机会被选中进行研究。

3.To avoid bias, the team implemented random sampling when selecting schools for their educational study.

为了避免偏见，团队在选择学校进行教育研究时实施了随机抽样。

4.The election committee conducted random sampling of voters to gauge public opinion on the proposed measures.

选举委员会对选民进行了随机抽样以评估公众对提议措施的看法。

5.By employing random sampling, the survey results were more representative of the entire population.

通过采用随机抽样，调查结果更能代表整个群体。

作文

Random sampling is a fundamental concept in statistics that plays a critical role in research and data collection. It refers to the method of selecting a subset of individuals from a larger population in such a way that every individual has an equal chance of being chosen. This technique is crucial because it helps to eliminate bias, ensuring that the sample accurately represents the population as a whole. By employing random sampling (随机抽样), researchers can draw more reliable conclusions and make generalizations about the entire population based on the analysis of the sample data.The importance of random sampling (随机抽样) cannot be overstated. Without it, studies may yield skewed results that do not reflect the true characteristics of the population. For instance, if a researcher were to survey only a specific group of people, such as those attending a particular event, the findings would likely be biased and not applicable to the broader community. In contrast, random sampling (随机抽样) allows for a diverse range of participants, which enhances the validity of the study.There are several methods of random sampling (随机抽样), including simple random sampling, stratified sampling, and cluster sampling. Simple random sampling involves selecting individuals entirely by chance, often using random number generators or drawing names from a hat. Stratified sampling, on the other hand, involves dividing the population into distinct subgroups and then randomly sampling from each subgroup. This method ensures that specific characteristics are represented proportionally in the sample. Finally, cluster sampling involves dividing the population into clusters, then randomly selecting entire clusters for inclusion in the study. Each method has its advantages and can be chosen based on the research objectives and the nature of the population.One of the key benefits of random sampling (随机抽样) is that it allows researchers to apply statistical techniques to analyze the data collected. Because the sample is representative, researchers can use inferential statistics to make predictions and draw conclusions about the entire population. This is particularly important in fields such as healthcare, social sciences, and market research, where understanding trends and behaviors is essential.Moreover, random sampling (随机抽样) contributes to the credibility of research findings. When studies are conducted with rigorous sampling methods, they gain greater acceptance among peers and stakeholders. This is crucial in influencing policy decisions, funding allocations, and public opinion. For example, public opinion polls that utilize random sampling (随机抽样) are often seen as more trustworthy than those that do not, as they reflect a broader spectrum of views.In conclusion, random sampling (随机抽样) is an essential technique in research that enhances the reliability and validity of findings. By ensuring that every individual in a population has an equal chance of being selected, researchers can draw meaningful conclusions that reflect the true nature of the population. As we continue to navigate an increasingly data-driven world, the principles of random sampling (随机抽样) will remain vital in ensuring that our understanding of various phenomena is accurate and representative. Therefore, mastering this concept is crucial for anyone involved in research and data analysis.

随机抽样是统计学中的一个基本概念，在研究和数据收集中起着至关重要的作用。它指的是从更大的人群中选择个体的方式，使每个个体都有相等的机会被选中。这种技术至关重要，因为它有助于消除偏见，确保样本准确代表整个群体。通过采用随机抽样（随机抽样），研究人员可以得出更可靠的结论，并根据样本数据的分析对整个群体进行概括。随机抽样（随机抽样）的重要性不容小觑。如果没有它，研究可能会产生扭曲的结果，这些结果并不反映人群的真实特征。例如，如果研究者只调查特定群体的人，例如参加某个特定活动的人，发现的结果可能会有偏差，无法适用于更广泛的社区。相比之下，随机抽样（随机抽样）允许参与者多样化，从而增强研究的有效性。随机抽样（随机抽样）有几种方法，包括简单随机抽样、分层抽样和聚类抽样。简单随机抽样涉及完全随机地选择个体，通常使用随机数生成器或从帽子里抽取姓名。另一方面，分层抽样涉及将人群划分为不同的子组，然后在每个子组中随机抽样。这种方法确保特定特征在样本中按比例代表。最后，聚类抽样涉及将人群划分为聚类，然后随机选择整个聚类以纳入研究。每种方法都有其优点，可以根据研究目标和人群的性质进行选择。随机抽样（随机抽样）的一个关键好处是它允许研究人员应用统计技术来分析收集的数据。由于样本具有代表性，研究人员可以使用推论统计来做出预测并得出关于整个群体的结论。这在医疗、社会科学和市场研究等领域尤为重要，因为了解趋势和行为至关重要。此外，随机抽样（随机抽样）有助于提高研究结果的可信度。当研究采用严格的抽样方法时，它们在同行和利益相关者中获得更大的接受度。这对于影响政策决策、资金分配和公众舆论至关重要。例如，利用随机抽样（随机抽样）的民意调查通常被视为比那些不这样做的调查更值得信赖，因为它们反映了更广泛的观点。总之，随机抽样（随机抽样）是研究中的一项基本技术，增强了发现的可靠性和有效性。通过确保人群中的每个个体都有相等的机会被选中，研究人员可以得出反映人群真实特征的有意义的结论。随着我们继续在一个越来越依赖数据的世界中航行，随机抽样（随机抽样）的原则将在确保我们对各种现象的理解准确且具有代表性方面保持重要。因此，掌握这一概念对于任何参与研究和数据分析的人来说都是至关重要的。